part ii lesson 11 a bit more solar pv, some verification & … · 2020. 1. 3. · 1.021, 3.021,...

38
1.021, 3.021, 10.333, 22.00 : Introduction to Modeling and Simulation : Spring 2012 Part II – Quantum Mechanical Methods : Lecture 11 A Bit More Solar PV, Some V&V and a Few Concluding Thoughts Jeffrey C. Grossman Department of Materials Science and Engineering Massachusetts Institute of Technology 1

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Page 1: Part II Lesson 11 A bit more solar PV, some verification & … · 2020. 1. 3. · 1.021, 3.021, 10.333, 22.00 : Introduction to Modeling and Simulation : Spring 2012. Part II –

1021 3021 10333 2200 Introduction to Modeling and Simulation Spring 2012

Part II ndash Quantum Mechanical Methods Lecture 11

A Bit More Solar PV Some VampV and a Few Concluding

Thoughts Jeffrey C Grossman

Department of Materials Science and Engineering Massachusetts Institute of Technology

1

11 Some PV Some VampV and Some Concluding Thoughts

Part II Topics 1 Itrsquos a Quantum WorldThe Theory of Quantum Mechanics

2 Quantum Mechanics Practice Makes Perfect

3 From Many-Body to Single-Particle Quantum Modeling of Molecules

4 Application of Quantum Modeling of Molecules Solar Thermal Fuels

5 Application of Quantum Modeling of Molecules Hydrogen Storage

6 From Atoms to Solids

7 Quantum Modeling of Solids Basic Properties

8 Advanced Prop of MaterialsWhat else can we do

9 Application of Quantum Modeling of Solids Solar Cells Part I

10 Application of Quantum Modeling of Solids Solar Cells Part IIApplication of Quantum Modeling of Solids Solar Cells Part II

Some PV Some VampV and Some Concluding Thoughts

2

Outline

bull Some more PV

bullVerification and Validation

bullA few more thoughts

3

Comparison of PV Technoloies

We are here eg bull amorphous silicon bull polymers bull all-carbon bull quantum dots

This image is in the public domain Source Wikimedia Commons

4

External Load

Ef

Ef

CBM

VBM

Photo-excitation Relaxation

Transport

Transport Recombination

Extraction

Extraction

Fundamental Processes Involved in Solar Photovoltaics Electronrsquos View

5

Crystalline Silicon Solar PV (80 of current market)

bull Light Absorption

bull Band Gap

bull Band Structure copy Helmut Fbll All rights reserved This content is excluded from our Creative

bull ElectronHole Transport Commons license For more information see httpocwmitedufairuse

bull ElectronHole Mobilities dk partf

σ = e 2τ minus v(k)v(k)4π3 partE

6

Amorphous Silicon Solar PV (3 of current market)

bull Light Absorption (is actually pretty good)

bull Electron-Hole Separation (also not a problem)

bull ElectronHole Transport (Holes are Slow)

bull Hole Mobilities

bull Hole Traps from total energy differences (Eneutral-Echarged)

7

Organic Solar PV

bull Light Absorption (need to capture more of the solar spectrum)

bull Band gap

bull Electron-Hole Separation

copy source unknown All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairusebull Orbital energies

Poly(3-hexylthiophene) (P3HT) Egexp = 21 eV Low-energy photons are not absorbed p

R

R

Egap = Eo Egap = 055Eo Egap = 11Eo 8

Dye Sensitized Solar PV

Gratzel and OrsquoRegan (Nature 1991)

Made up of 3 active materials bullDye absorbs light bullTiO2 nanoparticles with very large surface area take electron

bullLiquid electrolyte delivers new electron from cathode to dye

Image in the public domain Via M R Jones on Wikimedia Commons

wwwenergyercom 9

Dye Sensitized Solar PV

bull Biggest problem is a liquid electrolyte

bull Relative energy levels of TiO2 and dye also key

copy source unknown All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

10

Going High Efciency Fundamenta imit

Conduction band

Excess energy above Eg heat

Eg

= max VOC

Valence band

n

As band gap increases the maximum open circuit voltage increases but the fracton of the solar spectrum absorbed decreases

11

copy DOE Lewis Group at Caltech All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfaq-fair-use

Multi-Junction Solar PV

Figures removed due to copyright restrictions See httpwwwnexpwcomtechnology tmhtml

bull Light Absorption

bull Band gaps

bull Conductivity Across Interfaces

bull Band gaps Band structures

12

Key Mechanism in Organic Solar PV Charge Separation at the Interface

Charge separation at this interface is highly efficient --Why

What is the detailed N S Sariciftci et al Science 1992 258 1474

B Kraabel et al JCP 1996 104 4268 mechanism for this picture C J Brabec et al CPL 2001 340 232

13

Excited State

Charge separated state essentially degenerate with bridge state

002 eV

13 eV

Bridge state forms hybridization of P3HT π state and C60 t1u state

P3HT C60

Y Kanai and JCG NanoLett 2007 14

CNTjP3HT Metallic CNT Carbon nanotubes instead of C60 Very little success thus far

Ef near P3HT bull Large charge transfer to the π state metallic CNT (~03 electron)

bull Fermi level just above P3HT HOMO state

Figures removed due to copyright restrictions See bull No interface states are formedFigures 4 and 5 in Nano Letters 8 no 3 (2008) so no Ef pinning

bull Small built-in potential (006 eV) junction-induced exciton dissociation highly unlikely

CNTPolymer solar cells unlikely to work well with mixed CNT distribution

eg Kymakis E et al Rev Adv Mater Sci 2005 10 300

Y Kanai and JCG NanoLett (2008) 15

Using Computational Quantum Mechanics to Design New Mechanisms

What could this mean for a solar cell

VBM

CBM

Straight Wire

VBM

CBM

Tapered Wire

Axial charge redistribution due to quantum confinement

on

a on

Z Wu J B Neaton and JCG PRL 100 246804 (2008) Nanoletters (2009)

Majo po n a ad an ag no dop ng n d d

Electronhole13 charge13 separaampon13 through13 thermalizaampon

16

Amorphous Silicon PV The Slow Holes

08

06

04

02

0

-02

No dangling bonds Dangling bond

s

-6 -4 -2 0 2 Total energy (eV)

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

Ho

le t

rap

dep

th (

eV)

This type of bond switch can be as bad for hole mobility as

dangling bonds

17

Hole Traps Dangling Bond vs Strain

Dangling bond hole trap

Strain-only hole trap (stronger)

LK Wager and JCG Phys Rev Le 18

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

New Microscopic Picture of Holes in a-Si

Computational quantum mechanics shows that pressure can mitigate these traps

LK Wager and JCG Phys Rev Le 19

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

Another Thin-Film PV Example Hyper-Doped Silicon

bull Silicon doped with chalcogen atoms (sulfur selenium tellurium) to non-equilibrium concentrations

bull Strong optical response at photon energies where silicon is typically transparent

bull Promise as a photovoltaic material

copy source unknown All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairuse

20

Evolution of Density of States SiSe Si53Se1

Si431Se1 pure Silicon

21

Needing to Know Structure and Chemistry

Image of uantum vials in public domain Figure copy source unknown Data adapted from Physical Review B 60 no 2704 (1) All rightsreserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairuse 22

Needing to Know Structure and Chemistry

copy APS All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmitedufairuse

Clean Surface

Emits blue light

A Puzder et al Physical Review Letters 88 097401 (2002)

Oxygenated Cluster

Emits red light

23

rsha10
Rectangle
rsha10
Rectangle

Validation

How do we know when a simulation is right

24

Example Rayleigh-Taylor Instability

From Leo P KadanoffldquoThe Good the Bad and the Awful [ Scientific Simulation and Predictionrdquo

25

Example Rayleigh-Taylor Instability

bull Idea occurs anytime a dense heavy fluid is accelerated into a light fluid or lesser density

bull Slight perturbations to plane parallel interfaces are unstable hellipfingers grow into sets of intershypenetrating fingers

bull Observed in weather inversions salt domes star nebulae

bull How to model this process Requires solving hydrodynamics equations

26

Example Rayleigh-Taylor Instability

Photo sources copy PNAS (left) and IOP Publishing (right) All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

27

Example Rayleigh-Taylor Instability

Photo source copy IOP Publishing All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

28

Validation and Verification Verification and validation (VampV) are processes that help to ensure that models and simulations are correct and reliable

Verification ldquoDid I build the thing rightrdquo Have the model and simulation been built so that they fully satisfy the developerrsquos intent

Validation ldquoDid I build the right thingrdquo Will the model or simulation be able to adequately support its intended use Is its fidelity appropriate for that

after Dale Pace 29

Wersquove learned a lot

30

Some of the key remaining challenges

The electron correlation problem Only seriously affects a fraction of materials but that fraction tends to contain interesting physics and technological potential At this point there is no logical follow-up on LDAGGA

31

Some of the key remaining challenges

The time-scale problem Short (MD) and long (thermo) is not a problem Intermediate is a problem eg phase transformations

32

Some of the key remaining challenges

The knowledge problem To study an engineering propertybehavior with atomisticshyscale modeling one needs to understand somewhat what controls that property (in order to include the relevant boundary conditions)

eg If a property is controlled by impurities then intrinsic calculations will be irrelevant (Si conductivity)

33

Some of the key remaining challenges

The structure problem Even though the structure-property relation is a key tenet of Materials Science we have only very limited ability to predict structure (crystal structure amorphous microstructure )

34

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons

35

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons Courtesy of Patrick J Lynch License CC-BY

36

Computa(ons13 should13 not13 subs(tute13 for13 lack13 of13

knowledge

Computational modeling is very powerful but be smart

37

MIT OpenCourseWarehttpocwmitedu

3021J 1021J 10333J 18361J 2200J Introduction to Modelling and SimulationSpring 2012

For information about citing these materials or our Terms of Use visit httpocwmiteduterms

Page 2: Part II Lesson 11 A bit more solar PV, some verification & … · 2020. 1. 3. · 1.021, 3.021, 10.333, 22.00 : Introduction to Modeling and Simulation : Spring 2012. Part II –

11 Some PV Some VampV and Some Concluding Thoughts

Part II Topics 1 Itrsquos a Quantum WorldThe Theory of Quantum Mechanics

2 Quantum Mechanics Practice Makes Perfect

3 From Many-Body to Single-Particle Quantum Modeling of Molecules

4 Application of Quantum Modeling of Molecules Solar Thermal Fuels

5 Application of Quantum Modeling of Molecules Hydrogen Storage

6 From Atoms to Solids

7 Quantum Modeling of Solids Basic Properties

8 Advanced Prop of MaterialsWhat else can we do

9 Application of Quantum Modeling of Solids Solar Cells Part I

10 Application of Quantum Modeling of Solids Solar Cells Part IIApplication of Quantum Modeling of Solids Solar Cells Part II

Some PV Some VampV and Some Concluding Thoughts

2

Outline

bull Some more PV

bullVerification and Validation

bullA few more thoughts

3

Comparison of PV Technoloies

We are here eg bull amorphous silicon bull polymers bull all-carbon bull quantum dots

This image is in the public domain Source Wikimedia Commons

4

External Load

Ef

Ef

CBM

VBM

Photo-excitation Relaxation

Transport

Transport Recombination

Extraction

Extraction

Fundamental Processes Involved in Solar Photovoltaics Electronrsquos View

5

Crystalline Silicon Solar PV (80 of current market)

bull Light Absorption

bull Band Gap

bull Band Structure copy Helmut Fbll All rights reserved This content is excluded from our Creative

bull ElectronHole Transport Commons license For more information see httpocwmitedufairuse

bull ElectronHole Mobilities dk partf

σ = e 2τ minus v(k)v(k)4π3 partE

6

Amorphous Silicon Solar PV (3 of current market)

bull Light Absorption (is actually pretty good)

bull Electron-Hole Separation (also not a problem)

bull ElectronHole Transport (Holes are Slow)

bull Hole Mobilities

bull Hole Traps from total energy differences (Eneutral-Echarged)

7

Organic Solar PV

bull Light Absorption (need to capture more of the solar spectrum)

bull Band gap

bull Electron-Hole Separation

copy source unknown All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairusebull Orbital energies

Poly(3-hexylthiophene) (P3HT) Egexp = 21 eV Low-energy photons are not absorbed p

R

R

Egap = Eo Egap = 055Eo Egap = 11Eo 8

Dye Sensitized Solar PV

Gratzel and OrsquoRegan (Nature 1991)

Made up of 3 active materials bullDye absorbs light bullTiO2 nanoparticles with very large surface area take electron

bullLiquid electrolyte delivers new electron from cathode to dye

Image in the public domain Via M R Jones on Wikimedia Commons

wwwenergyercom 9

Dye Sensitized Solar PV

bull Biggest problem is a liquid electrolyte

bull Relative energy levels of TiO2 and dye also key

copy source unknown All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

10

Going High Efciency Fundamenta imit

Conduction band

Excess energy above Eg heat

Eg

= max VOC

Valence band

n

As band gap increases the maximum open circuit voltage increases but the fracton of the solar spectrum absorbed decreases

11

copy DOE Lewis Group at Caltech All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfaq-fair-use

Multi-Junction Solar PV

Figures removed due to copyright restrictions See httpwwwnexpwcomtechnology tmhtml

bull Light Absorption

bull Band gaps

bull Conductivity Across Interfaces

bull Band gaps Band structures

12

Key Mechanism in Organic Solar PV Charge Separation at the Interface

Charge separation at this interface is highly efficient --Why

What is the detailed N S Sariciftci et al Science 1992 258 1474

B Kraabel et al JCP 1996 104 4268 mechanism for this picture C J Brabec et al CPL 2001 340 232

13

Excited State

Charge separated state essentially degenerate with bridge state

002 eV

13 eV

Bridge state forms hybridization of P3HT π state and C60 t1u state

P3HT C60

Y Kanai and JCG NanoLett 2007 14

CNTjP3HT Metallic CNT Carbon nanotubes instead of C60 Very little success thus far

Ef near P3HT bull Large charge transfer to the π state metallic CNT (~03 electron)

bull Fermi level just above P3HT HOMO state

Figures removed due to copyright restrictions See bull No interface states are formedFigures 4 and 5 in Nano Letters 8 no 3 (2008) so no Ef pinning

bull Small built-in potential (006 eV) junction-induced exciton dissociation highly unlikely

CNTPolymer solar cells unlikely to work well with mixed CNT distribution

eg Kymakis E et al Rev Adv Mater Sci 2005 10 300

Y Kanai and JCG NanoLett (2008) 15

Using Computational Quantum Mechanics to Design New Mechanisms

What could this mean for a solar cell

VBM

CBM

Straight Wire

VBM

CBM

Tapered Wire

Axial charge redistribution due to quantum confinement

on

a on

Z Wu J B Neaton and JCG PRL 100 246804 (2008) Nanoletters (2009)

Majo po n a ad an ag no dop ng n d d

Electronhole13 charge13 separaampon13 through13 thermalizaampon

16

Amorphous Silicon PV The Slow Holes

08

06

04

02

0

-02

No dangling bonds Dangling bond

s

-6 -4 -2 0 2 Total energy (eV)

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

Ho

le t

rap

dep

th (

eV)

This type of bond switch can be as bad for hole mobility as

dangling bonds

17

Hole Traps Dangling Bond vs Strain

Dangling bond hole trap

Strain-only hole trap (stronger)

LK Wager and JCG Phys Rev Le 18

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

New Microscopic Picture of Holes in a-Si

Computational quantum mechanics shows that pressure can mitigate these traps

LK Wager and JCG Phys Rev Le 19

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

Another Thin-Film PV Example Hyper-Doped Silicon

bull Silicon doped with chalcogen atoms (sulfur selenium tellurium) to non-equilibrium concentrations

bull Strong optical response at photon energies where silicon is typically transparent

bull Promise as a photovoltaic material

copy source unknown All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairuse

20

Evolution of Density of States SiSe Si53Se1

Si431Se1 pure Silicon

21

Needing to Know Structure and Chemistry

Image of uantum vials in public domain Figure copy source unknown Data adapted from Physical Review B 60 no 2704 (1) All rightsreserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairuse 22

Needing to Know Structure and Chemistry

copy APS All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmitedufairuse

Clean Surface

Emits blue light

A Puzder et al Physical Review Letters 88 097401 (2002)

Oxygenated Cluster

Emits red light

23

rsha10
Rectangle
rsha10
Rectangle

Validation

How do we know when a simulation is right

24

Example Rayleigh-Taylor Instability

From Leo P KadanoffldquoThe Good the Bad and the Awful [ Scientific Simulation and Predictionrdquo

25

Example Rayleigh-Taylor Instability

bull Idea occurs anytime a dense heavy fluid is accelerated into a light fluid or lesser density

bull Slight perturbations to plane parallel interfaces are unstable hellipfingers grow into sets of intershypenetrating fingers

bull Observed in weather inversions salt domes star nebulae

bull How to model this process Requires solving hydrodynamics equations

26

Example Rayleigh-Taylor Instability

Photo sources copy PNAS (left) and IOP Publishing (right) All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

27

Example Rayleigh-Taylor Instability

Photo source copy IOP Publishing All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

28

Validation and Verification Verification and validation (VampV) are processes that help to ensure that models and simulations are correct and reliable

Verification ldquoDid I build the thing rightrdquo Have the model and simulation been built so that they fully satisfy the developerrsquos intent

Validation ldquoDid I build the right thingrdquo Will the model or simulation be able to adequately support its intended use Is its fidelity appropriate for that

after Dale Pace 29

Wersquove learned a lot

30

Some of the key remaining challenges

The electron correlation problem Only seriously affects a fraction of materials but that fraction tends to contain interesting physics and technological potential At this point there is no logical follow-up on LDAGGA

31

Some of the key remaining challenges

The time-scale problem Short (MD) and long (thermo) is not a problem Intermediate is a problem eg phase transformations

32

Some of the key remaining challenges

The knowledge problem To study an engineering propertybehavior with atomisticshyscale modeling one needs to understand somewhat what controls that property (in order to include the relevant boundary conditions)

eg If a property is controlled by impurities then intrinsic calculations will be irrelevant (Si conductivity)

33

Some of the key remaining challenges

The structure problem Even though the structure-property relation is a key tenet of Materials Science we have only very limited ability to predict structure (crystal structure amorphous microstructure )

34

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons

35

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons Courtesy of Patrick J Lynch License CC-BY

36

Computa(ons13 should13 not13 subs(tute13 for13 lack13 of13

knowledge

Computational modeling is very powerful but be smart

37

MIT OpenCourseWarehttpocwmitedu

3021J 1021J 10333J 18361J 2200J Introduction to Modelling and SimulationSpring 2012

For information about citing these materials or our Terms of Use visit httpocwmiteduterms

Page 3: Part II Lesson 11 A bit more solar PV, some verification & … · 2020. 1. 3. · 1.021, 3.021, 10.333, 22.00 : Introduction to Modeling and Simulation : Spring 2012. Part II –

Outline

bull Some more PV

bullVerification and Validation

bullA few more thoughts

3

Comparison of PV Technoloies

We are here eg bull amorphous silicon bull polymers bull all-carbon bull quantum dots

This image is in the public domain Source Wikimedia Commons

4

External Load

Ef

Ef

CBM

VBM

Photo-excitation Relaxation

Transport

Transport Recombination

Extraction

Extraction

Fundamental Processes Involved in Solar Photovoltaics Electronrsquos View

5

Crystalline Silicon Solar PV (80 of current market)

bull Light Absorption

bull Band Gap

bull Band Structure copy Helmut Fbll All rights reserved This content is excluded from our Creative

bull ElectronHole Transport Commons license For more information see httpocwmitedufairuse

bull ElectronHole Mobilities dk partf

σ = e 2τ minus v(k)v(k)4π3 partE

6

Amorphous Silicon Solar PV (3 of current market)

bull Light Absorption (is actually pretty good)

bull Electron-Hole Separation (also not a problem)

bull ElectronHole Transport (Holes are Slow)

bull Hole Mobilities

bull Hole Traps from total energy differences (Eneutral-Echarged)

7

Organic Solar PV

bull Light Absorption (need to capture more of the solar spectrum)

bull Band gap

bull Electron-Hole Separation

copy source unknown All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairusebull Orbital energies

Poly(3-hexylthiophene) (P3HT) Egexp = 21 eV Low-energy photons are not absorbed p

R

R

Egap = Eo Egap = 055Eo Egap = 11Eo 8

Dye Sensitized Solar PV

Gratzel and OrsquoRegan (Nature 1991)

Made up of 3 active materials bullDye absorbs light bullTiO2 nanoparticles with very large surface area take electron

bullLiquid electrolyte delivers new electron from cathode to dye

Image in the public domain Via M R Jones on Wikimedia Commons

wwwenergyercom 9

Dye Sensitized Solar PV

bull Biggest problem is a liquid electrolyte

bull Relative energy levels of TiO2 and dye also key

copy source unknown All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

10

Going High Efciency Fundamenta imit

Conduction band

Excess energy above Eg heat

Eg

= max VOC

Valence band

n

As band gap increases the maximum open circuit voltage increases but the fracton of the solar spectrum absorbed decreases

11

copy DOE Lewis Group at Caltech All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfaq-fair-use

Multi-Junction Solar PV

Figures removed due to copyright restrictions See httpwwwnexpwcomtechnology tmhtml

bull Light Absorption

bull Band gaps

bull Conductivity Across Interfaces

bull Band gaps Band structures

12

Key Mechanism in Organic Solar PV Charge Separation at the Interface

Charge separation at this interface is highly efficient --Why

What is the detailed N S Sariciftci et al Science 1992 258 1474

B Kraabel et al JCP 1996 104 4268 mechanism for this picture C J Brabec et al CPL 2001 340 232

13

Excited State

Charge separated state essentially degenerate with bridge state

002 eV

13 eV

Bridge state forms hybridization of P3HT π state and C60 t1u state

P3HT C60

Y Kanai and JCG NanoLett 2007 14

CNTjP3HT Metallic CNT Carbon nanotubes instead of C60 Very little success thus far

Ef near P3HT bull Large charge transfer to the π state metallic CNT (~03 electron)

bull Fermi level just above P3HT HOMO state

Figures removed due to copyright restrictions See bull No interface states are formedFigures 4 and 5 in Nano Letters 8 no 3 (2008) so no Ef pinning

bull Small built-in potential (006 eV) junction-induced exciton dissociation highly unlikely

CNTPolymer solar cells unlikely to work well with mixed CNT distribution

eg Kymakis E et al Rev Adv Mater Sci 2005 10 300

Y Kanai and JCG NanoLett (2008) 15

Using Computational Quantum Mechanics to Design New Mechanisms

What could this mean for a solar cell

VBM

CBM

Straight Wire

VBM

CBM

Tapered Wire

Axial charge redistribution due to quantum confinement

on

a on

Z Wu J B Neaton and JCG PRL 100 246804 (2008) Nanoletters (2009)

Majo po n a ad an ag no dop ng n d d

Electronhole13 charge13 separaampon13 through13 thermalizaampon

16

Amorphous Silicon PV The Slow Holes

08

06

04

02

0

-02

No dangling bonds Dangling bond

s

-6 -4 -2 0 2 Total energy (eV)

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

Ho

le t

rap

dep

th (

eV)

This type of bond switch can be as bad for hole mobility as

dangling bonds

17

Hole Traps Dangling Bond vs Strain

Dangling bond hole trap

Strain-only hole trap (stronger)

LK Wager and JCG Phys Rev Le 18

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

New Microscopic Picture of Holes in a-Si

Computational quantum mechanics shows that pressure can mitigate these traps

LK Wager and JCG Phys Rev Le 19

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

Another Thin-Film PV Example Hyper-Doped Silicon

bull Silicon doped with chalcogen atoms (sulfur selenium tellurium) to non-equilibrium concentrations

bull Strong optical response at photon energies where silicon is typically transparent

bull Promise as a photovoltaic material

copy source unknown All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairuse

20

Evolution of Density of States SiSe Si53Se1

Si431Se1 pure Silicon

21

Needing to Know Structure and Chemistry

Image of uantum vials in public domain Figure copy source unknown Data adapted from Physical Review B 60 no 2704 (1) All rightsreserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairuse 22

Needing to Know Structure and Chemistry

copy APS All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmitedufairuse

Clean Surface

Emits blue light

A Puzder et al Physical Review Letters 88 097401 (2002)

Oxygenated Cluster

Emits red light

23

rsha10
Rectangle
rsha10
Rectangle

Validation

How do we know when a simulation is right

24

Example Rayleigh-Taylor Instability

From Leo P KadanoffldquoThe Good the Bad and the Awful [ Scientific Simulation and Predictionrdquo

25

Example Rayleigh-Taylor Instability

bull Idea occurs anytime a dense heavy fluid is accelerated into a light fluid or lesser density

bull Slight perturbations to plane parallel interfaces are unstable hellipfingers grow into sets of intershypenetrating fingers

bull Observed in weather inversions salt domes star nebulae

bull How to model this process Requires solving hydrodynamics equations

26

Example Rayleigh-Taylor Instability

Photo sources copy PNAS (left) and IOP Publishing (right) All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

27

Example Rayleigh-Taylor Instability

Photo source copy IOP Publishing All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

28

Validation and Verification Verification and validation (VampV) are processes that help to ensure that models and simulations are correct and reliable

Verification ldquoDid I build the thing rightrdquo Have the model and simulation been built so that they fully satisfy the developerrsquos intent

Validation ldquoDid I build the right thingrdquo Will the model or simulation be able to adequately support its intended use Is its fidelity appropriate for that

after Dale Pace 29

Wersquove learned a lot

30

Some of the key remaining challenges

The electron correlation problem Only seriously affects a fraction of materials but that fraction tends to contain interesting physics and technological potential At this point there is no logical follow-up on LDAGGA

31

Some of the key remaining challenges

The time-scale problem Short (MD) and long (thermo) is not a problem Intermediate is a problem eg phase transformations

32

Some of the key remaining challenges

The knowledge problem To study an engineering propertybehavior with atomisticshyscale modeling one needs to understand somewhat what controls that property (in order to include the relevant boundary conditions)

eg If a property is controlled by impurities then intrinsic calculations will be irrelevant (Si conductivity)

33

Some of the key remaining challenges

The structure problem Even though the structure-property relation is a key tenet of Materials Science we have only very limited ability to predict structure (crystal structure amorphous microstructure )

34

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons

35

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons Courtesy of Patrick J Lynch License CC-BY

36

Computa(ons13 should13 not13 subs(tute13 for13 lack13 of13

knowledge

Computational modeling is very powerful but be smart

37

MIT OpenCourseWarehttpocwmitedu

3021J 1021J 10333J 18361J 2200J Introduction to Modelling and SimulationSpring 2012

For information about citing these materials or our Terms of Use visit httpocwmiteduterms

Page 4: Part II Lesson 11 A bit more solar PV, some verification & … · 2020. 1. 3. · 1.021, 3.021, 10.333, 22.00 : Introduction to Modeling and Simulation : Spring 2012. Part II –

Comparison of PV Technoloies

We are here eg bull amorphous silicon bull polymers bull all-carbon bull quantum dots

This image is in the public domain Source Wikimedia Commons

4

External Load

Ef

Ef

CBM

VBM

Photo-excitation Relaxation

Transport

Transport Recombination

Extraction

Extraction

Fundamental Processes Involved in Solar Photovoltaics Electronrsquos View

5

Crystalline Silicon Solar PV (80 of current market)

bull Light Absorption

bull Band Gap

bull Band Structure copy Helmut Fbll All rights reserved This content is excluded from our Creative

bull ElectronHole Transport Commons license For more information see httpocwmitedufairuse

bull ElectronHole Mobilities dk partf

σ = e 2τ minus v(k)v(k)4π3 partE

6

Amorphous Silicon Solar PV (3 of current market)

bull Light Absorption (is actually pretty good)

bull Electron-Hole Separation (also not a problem)

bull ElectronHole Transport (Holes are Slow)

bull Hole Mobilities

bull Hole Traps from total energy differences (Eneutral-Echarged)

7

Organic Solar PV

bull Light Absorption (need to capture more of the solar spectrum)

bull Band gap

bull Electron-Hole Separation

copy source unknown All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairusebull Orbital energies

Poly(3-hexylthiophene) (P3HT) Egexp = 21 eV Low-energy photons are not absorbed p

R

R

Egap = Eo Egap = 055Eo Egap = 11Eo 8

Dye Sensitized Solar PV

Gratzel and OrsquoRegan (Nature 1991)

Made up of 3 active materials bullDye absorbs light bullTiO2 nanoparticles with very large surface area take electron

bullLiquid electrolyte delivers new electron from cathode to dye

Image in the public domain Via M R Jones on Wikimedia Commons

wwwenergyercom 9

Dye Sensitized Solar PV

bull Biggest problem is a liquid electrolyte

bull Relative energy levels of TiO2 and dye also key

copy source unknown All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

10

Going High Efciency Fundamenta imit

Conduction band

Excess energy above Eg heat

Eg

= max VOC

Valence band

n

As band gap increases the maximum open circuit voltage increases but the fracton of the solar spectrum absorbed decreases

11

copy DOE Lewis Group at Caltech All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfaq-fair-use

Multi-Junction Solar PV

Figures removed due to copyright restrictions See httpwwwnexpwcomtechnology tmhtml

bull Light Absorption

bull Band gaps

bull Conductivity Across Interfaces

bull Band gaps Band structures

12

Key Mechanism in Organic Solar PV Charge Separation at the Interface

Charge separation at this interface is highly efficient --Why

What is the detailed N S Sariciftci et al Science 1992 258 1474

B Kraabel et al JCP 1996 104 4268 mechanism for this picture C J Brabec et al CPL 2001 340 232

13

Excited State

Charge separated state essentially degenerate with bridge state

002 eV

13 eV

Bridge state forms hybridization of P3HT π state and C60 t1u state

P3HT C60

Y Kanai and JCG NanoLett 2007 14

CNTjP3HT Metallic CNT Carbon nanotubes instead of C60 Very little success thus far

Ef near P3HT bull Large charge transfer to the π state metallic CNT (~03 electron)

bull Fermi level just above P3HT HOMO state

Figures removed due to copyright restrictions See bull No interface states are formedFigures 4 and 5 in Nano Letters 8 no 3 (2008) so no Ef pinning

bull Small built-in potential (006 eV) junction-induced exciton dissociation highly unlikely

CNTPolymer solar cells unlikely to work well with mixed CNT distribution

eg Kymakis E et al Rev Adv Mater Sci 2005 10 300

Y Kanai and JCG NanoLett (2008) 15

Using Computational Quantum Mechanics to Design New Mechanisms

What could this mean for a solar cell

VBM

CBM

Straight Wire

VBM

CBM

Tapered Wire

Axial charge redistribution due to quantum confinement

on

a on

Z Wu J B Neaton and JCG PRL 100 246804 (2008) Nanoletters (2009)

Majo po n a ad an ag no dop ng n d d

Electronhole13 charge13 separaampon13 through13 thermalizaampon

16

Amorphous Silicon PV The Slow Holes

08

06

04

02

0

-02

No dangling bonds Dangling bond

s

-6 -4 -2 0 2 Total energy (eV)

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

Ho

le t

rap

dep

th (

eV)

This type of bond switch can be as bad for hole mobility as

dangling bonds

17

Hole Traps Dangling Bond vs Strain

Dangling bond hole trap

Strain-only hole trap (stronger)

LK Wager and JCG Phys Rev Le 18

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

New Microscopic Picture of Holes in a-Si

Computational quantum mechanics shows that pressure can mitigate these traps

LK Wager and JCG Phys Rev Le 19

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

Another Thin-Film PV Example Hyper-Doped Silicon

bull Silicon doped with chalcogen atoms (sulfur selenium tellurium) to non-equilibrium concentrations

bull Strong optical response at photon energies where silicon is typically transparent

bull Promise as a photovoltaic material

copy source unknown All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairuse

20

Evolution of Density of States SiSe Si53Se1

Si431Se1 pure Silicon

21

Needing to Know Structure and Chemistry

Image of uantum vials in public domain Figure copy source unknown Data adapted from Physical Review B 60 no 2704 (1) All rightsreserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairuse 22

Needing to Know Structure and Chemistry

copy APS All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmitedufairuse

Clean Surface

Emits blue light

A Puzder et al Physical Review Letters 88 097401 (2002)

Oxygenated Cluster

Emits red light

23

rsha10
Rectangle
rsha10
Rectangle

Validation

How do we know when a simulation is right

24

Example Rayleigh-Taylor Instability

From Leo P KadanoffldquoThe Good the Bad and the Awful [ Scientific Simulation and Predictionrdquo

25

Example Rayleigh-Taylor Instability

bull Idea occurs anytime a dense heavy fluid is accelerated into a light fluid or lesser density

bull Slight perturbations to plane parallel interfaces are unstable hellipfingers grow into sets of intershypenetrating fingers

bull Observed in weather inversions salt domes star nebulae

bull How to model this process Requires solving hydrodynamics equations

26

Example Rayleigh-Taylor Instability

Photo sources copy PNAS (left) and IOP Publishing (right) All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

27

Example Rayleigh-Taylor Instability

Photo source copy IOP Publishing All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

28

Validation and Verification Verification and validation (VampV) are processes that help to ensure that models and simulations are correct and reliable

Verification ldquoDid I build the thing rightrdquo Have the model and simulation been built so that they fully satisfy the developerrsquos intent

Validation ldquoDid I build the right thingrdquo Will the model or simulation be able to adequately support its intended use Is its fidelity appropriate for that

after Dale Pace 29

Wersquove learned a lot

30

Some of the key remaining challenges

The electron correlation problem Only seriously affects a fraction of materials but that fraction tends to contain interesting physics and technological potential At this point there is no logical follow-up on LDAGGA

31

Some of the key remaining challenges

The time-scale problem Short (MD) and long (thermo) is not a problem Intermediate is a problem eg phase transformations

32

Some of the key remaining challenges

The knowledge problem To study an engineering propertybehavior with atomisticshyscale modeling one needs to understand somewhat what controls that property (in order to include the relevant boundary conditions)

eg If a property is controlled by impurities then intrinsic calculations will be irrelevant (Si conductivity)

33

Some of the key remaining challenges

The structure problem Even though the structure-property relation is a key tenet of Materials Science we have only very limited ability to predict structure (crystal structure amorphous microstructure )

34

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons

35

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons Courtesy of Patrick J Lynch License CC-BY

36

Computa(ons13 should13 not13 subs(tute13 for13 lack13 of13

knowledge

Computational modeling is very powerful but be smart

37

MIT OpenCourseWarehttpocwmitedu

3021J 1021J 10333J 18361J 2200J Introduction to Modelling and SimulationSpring 2012

For information about citing these materials or our Terms of Use visit httpocwmiteduterms

Page 5: Part II Lesson 11 A bit more solar PV, some verification & … · 2020. 1. 3. · 1.021, 3.021, 10.333, 22.00 : Introduction to Modeling and Simulation : Spring 2012. Part II –

External Load

Ef

Ef

CBM

VBM

Photo-excitation Relaxation

Transport

Transport Recombination

Extraction

Extraction

Fundamental Processes Involved in Solar Photovoltaics Electronrsquos View

5

Crystalline Silicon Solar PV (80 of current market)

bull Light Absorption

bull Band Gap

bull Band Structure copy Helmut Fbll All rights reserved This content is excluded from our Creative

bull ElectronHole Transport Commons license For more information see httpocwmitedufairuse

bull ElectronHole Mobilities dk partf

σ = e 2τ minus v(k)v(k)4π3 partE

6

Amorphous Silicon Solar PV (3 of current market)

bull Light Absorption (is actually pretty good)

bull Electron-Hole Separation (also not a problem)

bull ElectronHole Transport (Holes are Slow)

bull Hole Mobilities

bull Hole Traps from total energy differences (Eneutral-Echarged)

7

Organic Solar PV

bull Light Absorption (need to capture more of the solar spectrum)

bull Band gap

bull Electron-Hole Separation

copy source unknown All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairusebull Orbital energies

Poly(3-hexylthiophene) (P3HT) Egexp = 21 eV Low-energy photons are not absorbed p

R

R

Egap = Eo Egap = 055Eo Egap = 11Eo 8

Dye Sensitized Solar PV

Gratzel and OrsquoRegan (Nature 1991)

Made up of 3 active materials bullDye absorbs light bullTiO2 nanoparticles with very large surface area take electron

bullLiquid electrolyte delivers new electron from cathode to dye

Image in the public domain Via M R Jones on Wikimedia Commons

wwwenergyercom 9

Dye Sensitized Solar PV

bull Biggest problem is a liquid electrolyte

bull Relative energy levels of TiO2 and dye also key

copy source unknown All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

10

Going High Efciency Fundamenta imit

Conduction band

Excess energy above Eg heat

Eg

= max VOC

Valence band

n

As band gap increases the maximum open circuit voltage increases but the fracton of the solar spectrum absorbed decreases

11

copy DOE Lewis Group at Caltech All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfaq-fair-use

Multi-Junction Solar PV

Figures removed due to copyright restrictions See httpwwwnexpwcomtechnology tmhtml

bull Light Absorption

bull Band gaps

bull Conductivity Across Interfaces

bull Band gaps Band structures

12

Key Mechanism in Organic Solar PV Charge Separation at the Interface

Charge separation at this interface is highly efficient --Why

What is the detailed N S Sariciftci et al Science 1992 258 1474

B Kraabel et al JCP 1996 104 4268 mechanism for this picture C J Brabec et al CPL 2001 340 232

13

Excited State

Charge separated state essentially degenerate with bridge state

002 eV

13 eV

Bridge state forms hybridization of P3HT π state and C60 t1u state

P3HT C60

Y Kanai and JCG NanoLett 2007 14

CNTjP3HT Metallic CNT Carbon nanotubes instead of C60 Very little success thus far

Ef near P3HT bull Large charge transfer to the π state metallic CNT (~03 electron)

bull Fermi level just above P3HT HOMO state

Figures removed due to copyright restrictions See bull No interface states are formedFigures 4 and 5 in Nano Letters 8 no 3 (2008) so no Ef pinning

bull Small built-in potential (006 eV) junction-induced exciton dissociation highly unlikely

CNTPolymer solar cells unlikely to work well with mixed CNT distribution

eg Kymakis E et al Rev Adv Mater Sci 2005 10 300

Y Kanai and JCG NanoLett (2008) 15

Using Computational Quantum Mechanics to Design New Mechanisms

What could this mean for a solar cell

VBM

CBM

Straight Wire

VBM

CBM

Tapered Wire

Axial charge redistribution due to quantum confinement

on

a on

Z Wu J B Neaton and JCG PRL 100 246804 (2008) Nanoletters (2009)

Majo po n a ad an ag no dop ng n d d

Electronhole13 charge13 separaampon13 through13 thermalizaampon

16

Amorphous Silicon PV The Slow Holes

08

06

04

02

0

-02

No dangling bonds Dangling bond

s

-6 -4 -2 0 2 Total energy (eV)

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

Ho

le t

rap

dep

th (

eV)

This type of bond switch can be as bad for hole mobility as

dangling bonds

17

Hole Traps Dangling Bond vs Strain

Dangling bond hole trap

Strain-only hole trap (stronger)

LK Wager and JCG Phys Rev Le 18

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

New Microscopic Picture of Holes in a-Si

Computational quantum mechanics shows that pressure can mitigate these traps

LK Wager and JCG Phys Rev Le 19

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

Another Thin-Film PV Example Hyper-Doped Silicon

bull Silicon doped with chalcogen atoms (sulfur selenium tellurium) to non-equilibrium concentrations

bull Strong optical response at photon energies where silicon is typically transparent

bull Promise as a photovoltaic material

copy source unknown All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairuse

20

Evolution of Density of States SiSe Si53Se1

Si431Se1 pure Silicon

21

Needing to Know Structure and Chemistry

Image of uantum vials in public domain Figure copy source unknown Data adapted from Physical Review B 60 no 2704 (1) All rightsreserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairuse 22

Needing to Know Structure and Chemistry

copy APS All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmitedufairuse

Clean Surface

Emits blue light

A Puzder et al Physical Review Letters 88 097401 (2002)

Oxygenated Cluster

Emits red light

23

rsha10
Rectangle
rsha10
Rectangle

Validation

How do we know when a simulation is right

24

Example Rayleigh-Taylor Instability

From Leo P KadanoffldquoThe Good the Bad and the Awful [ Scientific Simulation and Predictionrdquo

25

Example Rayleigh-Taylor Instability

bull Idea occurs anytime a dense heavy fluid is accelerated into a light fluid or lesser density

bull Slight perturbations to plane parallel interfaces are unstable hellipfingers grow into sets of intershypenetrating fingers

bull Observed in weather inversions salt domes star nebulae

bull How to model this process Requires solving hydrodynamics equations

26

Example Rayleigh-Taylor Instability

Photo sources copy PNAS (left) and IOP Publishing (right) All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

27

Example Rayleigh-Taylor Instability

Photo source copy IOP Publishing All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

28

Validation and Verification Verification and validation (VampV) are processes that help to ensure that models and simulations are correct and reliable

Verification ldquoDid I build the thing rightrdquo Have the model and simulation been built so that they fully satisfy the developerrsquos intent

Validation ldquoDid I build the right thingrdquo Will the model or simulation be able to adequately support its intended use Is its fidelity appropriate for that

after Dale Pace 29

Wersquove learned a lot

30

Some of the key remaining challenges

The electron correlation problem Only seriously affects a fraction of materials but that fraction tends to contain interesting physics and technological potential At this point there is no logical follow-up on LDAGGA

31

Some of the key remaining challenges

The time-scale problem Short (MD) and long (thermo) is not a problem Intermediate is a problem eg phase transformations

32

Some of the key remaining challenges

The knowledge problem To study an engineering propertybehavior with atomisticshyscale modeling one needs to understand somewhat what controls that property (in order to include the relevant boundary conditions)

eg If a property is controlled by impurities then intrinsic calculations will be irrelevant (Si conductivity)

33

Some of the key remaining challenges

The structure problem Even though the structure-property relation is a key tenet of Materials Science we have only very limited ability to predict structure (crystal structure amorphous microstructure )

34

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons

35

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons Courtesy of Patrick J Lynch License CC-BY

36

Computa(ons13 should13 not13 subs(tute13 for13 lack13 of13

knowledge

Computational modeling is very powerful but be smart

37

MIT OpenCourseWarehttpocwmitedu

3021J 1021J 10333J 18361J 2200J Introduction to Modelling and SimulationSpring 2012

For information about citing these materials or our Terms of Use visit httpocwmiteduterms

Page 6: Part II Lesson 11 A bit more solar PV, some verification & … · 2020. 1. 3. · 1.021, 3.021, 10.333, 22.00 : Introduction to Modeling and Simulation : Spring 2012. Part II –

Crystalline Silicon Solar PV (80 of current market)

bull Light Absorption

bull Band Gap

bull Band Structure copy Helmut Fbll All rights reserved This content is excluded from our Creative

bull ElectronHole Transport Commons license For more information see httpocwmitedufairuse

bull ElectronHole Mobilities dk partf

σ = e 2τ minus v(k)v(k)4π3 partE

6

Amorphous Silicon Solar PV (3 of current market)

bull Light Absorption (is actually pretty good)

bull Electron-Hole Separation (also not a problem)

bull ElectronHole Transport (Holes are Slow)

bull Hole Mobilities

bull Hole Traps from total energy differences (Eneutral-Echarged)

7

Organic Solar PV

bull Light Absorption (need to capture more of the solar spectrum)

bull Band gap

bull Electron-Hole Separation

copy source unknown All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairusebull Orbital energies

Poly(3-hexylthiophene) (P3HT) Egexp = 21 eV Low-energy photons are not absorbed p

R

R

Egap = Eo Egap = 055Eo Egap = 11Eo 8

Dye Sensitized Solar PV

Gratzel and OrsquoRegan (Nature 1991)

Made up of 3 active materials bullDye absorbs light bullTiO2 nanoparticles with very large surface area take electron

bullLiquid electrolyte delivers new electron from cathode to dye

Image in the public domain Via M R Jones on Wikimedia Commons

wwwenergyercom 9

Dye Sensitized Solar PV

bull Biggest problem is a liquid electrolyte

bull Relative energy levels of TiO2 and dye also key

copy source unknown All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

10

Going High Efciency Fundamenta imit

Conduction band

Excess energy above Eg heat

Eg

= max VOC

Valence band

n

As band gap increases the maximum open circuit voltage increases but the fracton of the solar spectrum absorbed decreases

11

copy DOE Lewis Group at Caltech All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfaq-fair-use

Multi-Junction Solar PV

Figures removed due to copyright restrictions See httpwwwnexpwcomtechnology tmhtml

bull Light Absorption

bull Band gaps

bull Conductivity Across Interfaces

bull Band gaps Band structures

12

Key Mechanism in Organic Solar PV Charge Separation at the Interface

Charge separation at this interface is highly efficient --Why

What is the detailed N S Sariciftci et al Science 1992 258 1474

B Kraabel et al JCP 1996 104 4268 mechanism for this picture C J Brabec et al CPL 2001 340 232

13

Excited State

Charge separated state essentially degenerate with bridge state

002 eV

13 eV

Bridge state forms hybridization of P3HT π state and C60 t1u state

P3HT C60

Y Kanai and JCG NanoLett 2007 14

CNTjP3HT Metallic CNT Carbon nanotubes instead of C60 Very little success thus far

Ef near P3HT bull Large charge transfer to the π state metallic CNT (~03 electron)

bull Fermi level just above P3HT HOMO state

Figures removed due to copyright restrictions See bull No interface states are formedFigures 4 and 5 in Nano Letters 8 no 3 (2008) so no Ef pinning

bull Small built-in potential (006 eV) junction-induced exciton dissociation highly unlikely

CNTPolymer solar cells unlikely to work well with mixed CNT distribution

eg Kymakis E et al Rev Adv Mater Sci 2005 10 300

Y Kanai and JCG NanoLett (2008) 15

Using Computational Quantum Mechanics to Design New Mechanisms

What could this mean for a solar cell

VBM

CBM

Straight Wire

VBM

CBM

Tapered Wire

Axial charge redistribution due to quantum confinement

on

a on

Z Wu J B Neaton and JCG PRL 100 246804 (2008) Nanoletters (2009)

Majo po n a ad an ag no dop ng n d d

Electronhole13 charge13 separaampon13 through13 thermalizaampon

16

Amorphous Silicon PV The Slow Holes

08

06

04

02

0

-02

No dangling bonds Dangling bond

s

-6 -4 -2 0 2 Total energy (eV)

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

Ho

le t

rap

dep

th (

eV)

This type of bond switch can be as bad for hole mobility as

dangling bonds

17

Hole Traps Dangling Bond vs Strain

Dangling bond hole trap

Strain-only hole trap (stronger)

LK Wager and JCG Phys Rev Le 18

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

New Microscopic Picture of Holes in a-Si

Computational quantum mechanics shows that pressure can mitigate these traps

LK Wager and JCG Phys Rev Le 19

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

Another Thin-Film PV Example Hyper-Doped Silicon

bull Silicon doped with chalcogen atoms (sulfur selenium tellurium) to non-equilibrium concentrations

bull Strong optical response at photon energies where silicon is typically transparent

bull Promise as a photovoltaic material

copy source unknown All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairuse

20

Evolution of Density of States SiSe Si53Se1

Si431Se1 pure Silicon

21

Needing to Know Structure and Chemistry

Image of uantum vials in public domain Figure copy source unknown Data adapted from Physical Review B 60 no 2704 (1) All rightsreserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairuse 22

Needing to Know Structure and Chemistry

copy APS All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmitedufairuse

Clean Surface

Emits blue light

A Puzder et al Physical Review Letters 88 097401 (2002)

Oxygenated Cluster

Emits red light

23

rsha10
Rectangle
rsha10
Rectangle

Validation

How do we know when a simulation is right

24

Example Rayleigh-Taylor Instability

From Leo P KadanoffldquoThe Good the Bad and the Awful [ Scientific Simulation and Predictionrdquo

25

Example Rayleigh-Taylor Instability

bull Idea occurs anytime a dense heavy fluid is accelerated into a light fluid or lesser density

bull Slight perturbations to plane parallel interfaces are unstable hellipfingers grow into sets of intershypenetrating fingers

bull Observed in weather inversions salt domes star nebulae

bull How to model this process Requires solving hydrodynamics equations

26

Example Rayleigh-Taylor Instability

Photo sources copy PNAS (left) and IOP Publishing (right) All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

27

Example Rayleigh-Taylor Instability

Photo source copy IOP Publishing All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

28

Validation and Verification Verification and validation (VampV) are processes that help to ensure that models and simulations are correct and reliable

Verification ldquoDid I build the thing rightrdquo Have the model and simulation been built so that they fully satisfy the developerrsquos intent

Validation ldquoDid I build the right thingrdquo Will the model or simulation be able to adequately support its intended use Is its fidelity appropriate for that

after Dale Pace 29

Wersquove learned a lot

30

Some of the key remaining challenges

The electron correlation problem Only seriously affects a fraction of materials but that fraction tends to contain interesting physics and technological potential At this point there is no logical follow-up on LDAGGA

31

Some of the key remaining challenges

The time-scale problem Short (MD) and long (thermo) is not a problem Intermediate is a problem eg phase transformations

32

Some of the key remaining challenges

The knowledge problem To study an engineering propertybehavior with atomisticshyscale modeling one needs to understand somewhat what controls that property (in order to include the relevant boundary conditions)

eg If a property is controlled by impurities then intrinsic calculations will be irrelevant (Si conductivity)

33

Some of the key remaining challenges

The structure problem Even though the structure-property relation is a key tenet of Materials Science we have only very limited ability to predict structure (crystal structure amorphous microstructure )

34

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons

35

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons Courtesy of Patrick J Lynch License CC-BY

36

Computa(ons13 should13 not13 subs(tute13 for13 lack13 of13

knowledge

Computational modeling is very powerful but be smart

37

MIT OpenCourseWarehttpocwmitedu

3021J 1021J 10333J 18361J 2200J Introduction to Modelling and SimulationSpring 2012

For information about citing these materials or our Terms of Use visit httpocwmiteduterms

Page 7: Part II Lesson 11 A bit more solar PV, some verification & … · 2020. 1. 3. · 1.021, 3.021, 10.333, 22.00 : Introduction to Modeling and Simulation : Spring 2012. Part II –

Amorphous Silicon Solar PV (3 of current market)

bull Light Absorption (is actually pretty good)

bull Electron-Hole Separation (also not a problem)

bull ElectronHole Transport (Holes are Slow)

bull Hole Mobilities

bull Hole Traps from total energy differences (Eneutral-Echarged)

7

Organic Solar PV

bull Light Absorption (need to capture more of the solar spectrum)

bull Band gap

bull Electron-Hole Separation

copy source unknown All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairusebull Orbital energies

Poly(3-hexylthiophene) (P3HT) Egexp = 21 eV Low-energy photons are not absorbed p

R

R

Egap = Eo Egap = 055Eo Egap = 11Eo 8

Dye Sensitized Solar PV

Gratzel and OrsquoRegan (Nature 1991)

Made up of 3 active materials bullDye absorbs light bullTiO2 nanoparticles with very large surface area take electron

bullLiquid electrolyte delivers new electron from cathode to dye

Image in the public domain Via M R Jones on Wikimedia Commons

wwwenergyercom 9

Dye Sensitized Solar PV

bull Biggest problem is a liquid electrolyte

bull Relative energy levels of TiO2 and dye also key

copy source unknown All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

10

Going High Efciency Fundamenta imit

Conduction band

Excess energy above Eg heat

Eg

= max VOC

Valence band

n

As band gap increases the maximum open circuit voltage increases but the fracton of the solar spectrum absorbed decreases

11

copy DOE Lewis Group at Caltech All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfaq-fair-use

Multi-Junction Solar PV

Figures removed due to copyright restrictions See httpwwwnexpwcomtechnology tmhtml

bull Light Absorption

bull Band gaps

bull Conductivity Across Interfaces

bull Band gaps Band structures

12

Key Mechanism in Organic Solar PV Charge Separation at the Interface

Charge separation at this interface is highly efficient --Why

What is the detailed N S Sariciftci et al Science 1992 258 1474

B Kraabel et al JCP 1996 104 4268 mechanism for this picture C J Brabec et al CPL 2001 340 232

13

Excited State

Charge separated state essentially degenerate with bridge state

002 eV

13 eV

Bridge state forms hybridization of P3HT π state and C60 t1u state

P3HT C60

Y Kanai and JCG NanoLett 2007 14

CNTjP3HT Metallic CNT Carbon nanotubes instead of C60 Very little success thus far

Ef near P3HT bull Large charge transfer to the π state metallic CNT (~03 electron)

bull Fermi level just above P3HT HOMO state

Figures removed due to copyright restrictions See bull No interface states are formedFigures 4 and 5 in Nano Letters 8 no 3 (2008) so no Ef pinning

bull Small built-in potential (006 eV) junction-induced exciton dissociation highly unlikely

CNTPolymer solar cells unlikely to work well with mixed CNT distribution

eg Kymakis E et al Rev Adv Mater Sci 2005 10 300

Y Kanai and JCG NanoLett (2008) 15

Using Computational Quantum Mechanics to Design New Mechanisms

What could this mean for a solar cell

VBM

CBM

Straight Wire

VBM

CBM

Tapered Wire

Axial charge redistribution due to quantum confinement

on

a on

Z Wu J B Neaton and JCG PRL 100 246804 (2008) Nanoletters (2009)

Majo po n a ad an ag no dop ng n d d

Electronhole13 charge13 separaampon13 through13 thermalizaampon

16

Amorphous Silicon PV The Slow Holes

08

06

04

02

0

-02

No dangling bonds Dangling bond

s

-6 -4 -2 0 2 Total energy (eV)

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

Ho

le t

rap

dep

th (

eV)

This type of bond switch can be as bad for hole mobility as

dangling bonds

17

Hole Traps Dangling Bond vs Strain

Dangling bond hole trap

Strain-only hole trap (stronger)

LK Wager and JCG Phys Rev Le 18

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

New Microscopic Picture of Holes in a-Si

Computational quantum mechanics shows that pressure can mitigate these traps

LK Wager and JCG Phys Rev Le 19

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

Another Thin-Film PV Example Hyper-Doped Silicon

bull Silicon doped with chalcogen atoms (sulfur selenium tellurium) to non-equilibrium concentrations

bull Strong optical response at photon energies where silicon is typically transparent

bull Promise as a photovoltaic material

copy source unknown All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairuse

20

Evolution of Density of States SiSe Si53Se1

Si431Se1 pure Silicon

21

Needing to Know Structure and Chemistry

Image of uantum vials in public domain Figure copy source unknown Data adapted from Physical Review B 60 no 2704 (1) All rightsreserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairuse 22

Needing to Know Structure and Chemistry

copy APS All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmitedufairuse

Clean Surface

Emits blue light

A Puzder et al Physical Review Letters 88 097401 (2002)

Oxygenated Cluster

Emits red light

23

rsha10
Rectangle
rsha10
Rectangle

Validation

How do we know when a simulation is right

24

Example Rayleigh-Taylor Instability

From Leo P KadanoffldquoThe Good the Bad and the Awful [ Scientific Simulation and Predictionrdquo

25

Example Rayleigh-Taylor Instability

bull Idea occurs anytime a dense heavy fluid is accelerated into a light fluid or lesser density

bull Slight perturbations to plane parallel interfaces are unstable hellipfingers grow into sets of intershypenetrating fingers

bull Observed in weather inversions salt domes star nebulae

bull How to model this process Requires solving hydrodynamics equations

26

Example Rayleigh-Taylor Instability

Photo sources copy PNAS (left) and IOP Publishing (right) All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

27

Example Rayleigh-Taylor Instability

Photo source copy IOP Publishing All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

28

Validation and Verification Verification and validation (VampV) are processes that help to ensure that models and simulations are correct and reliable

Verification ldquoDid I build the thing rightrdquo Have the model and simulation been built so that they fully satisfy the developerrsquos intent

Validation ldquoDid I build the right thingrdquo Will the model or simulation be able to adequately support its intended use Is its fidelity appropriate for that

after Dale Pace 29

Wersquove learned a lot

30

Some of the key remaining challenges

The electron correlation problem Only seriously affects a fraction of materials but that fraction tends to contain interesting physics and technological potential At this point there is no logical follow-up on LDAGGA

31

Some of the key remaining challenges

The time-scale problem Short (MD) and long (thermo) is not a problem Intermediate is a problem eg phase transformations

32

Some of the key remaining challenges

The knowledge problem To study an engineering propertybehavior with atomisticshyscale modeling one needs to understand somewhat what controls that property (in order to include the relevant boundary conditions)

eg If a property is controlled by impurities then intrinsic calculations will be irrelevant (Si conductivity)

33

Some of the key remaining challenges

The structure problem Even though the structure-property relation is a key tenet of Materials Science we have only very limited ability to predict structure (crystal structure amorphous microstructure )

34

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons

35

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons Courtesy of Patrick J Lynch License CC-BY

36

Computa(ons13 should13 not13 subs(tute13 for13 lack13 of13

knowledge

Computational modeling is very powerful but be smart

37

MIT OpenCourseWarehttpocwmitedu

3021J 1021J 10333J 18361J 2200J Introduction to Modelling and SimulationSpring 2012

For information about citing these materials or our Terms of Use visit httpocwmiteduterms

Page 8: Part II Lesson 11 A bit more solar PV, some verification & … · 2020. 1. 3. · 1.021, 3.021, 10.333, 22.00 : Introduction to Modeling and Simulation : Spring 2012. Part II –

Organic Solar PV

bull Light Absorption (need to capture more of the solar spectrum)

bull Band gap

bull Electron-Hole Separation

copy source unknown All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairusebull Orbital energies

Poly(3-hexylthiophene) (P3HT) Egexp = 21 eV Low-energy photons are not absorbed p

R

R

Egap = Eo Egap = 055Eo Egap = 11Eo 8

Dye Sensitized Solar PV

Gratzel and OrsquoRegan (Nature 1991)

Made up of 3 active materials bullDye absorbs light bullTiO2 nanoparticles with very large surface area take electron

bullLiquid electrolyte delivers new electron from cathode to dye

Image in the public domain Via M R Jones on Wikimedia Commons

wwwenergyercom 9

Dye Sensitized Solar PV

bull Biggest problem is a liquid electrolyte

bull Relative energy levels of TiO2 and dye also key

copy source unknown All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

10

Going High Efciency Fundamenta imit

Conduction band

Excess energy above Eg heat

Eg

= max VOC

Valence band

n

As band gap increases the maximum open circuit voltage increases but the fracton of the solar spectrum absorbed decreases

11

copy DOE Lewis Group at Caltech All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfaq-fair-use

Multi-Junction Solar PV

Figures removed due to copyright restrictions See httpwwwnexpwcomtechnology tmhtml

bull Light Absorption

bull Band gaps

bull Conductivity Across Interfaces

bull Band gaps Band structures

12

Key Mechanism in Organic Solar PV Charge Separation at the Interface

Charge separation at this interface is highly efficient --Why

What is the detailed N S Sariciftci et al Science 1992 258 1474

B Kraabel et al JCP 1996 104 4268 mechanism for this picture C J Brabec et al CPL 2001 340 232

13

Excited State

Charge separated state essentially degenerate with bridge state

002 eV

13 eV

Bridge state forms hybridization of P3HT π state and C60 t1u state

P3HT C60

Y Kanai and JCG NanoLett 2007 14

CNTjP3HT Metallic CNT Carbon nanotubes instead of C60 Very little success thus far

Ef near P3HT bull Large charge transfer to the π state metallic CNT (~03 electron)

bull Fermi level just above P3HT HOMO state

Figures removed due to copyright restrictions See bull No interface states are formedFigures 4 and 5 in Nano Letters 8 no 3 (2008) so no Ef pinning

bull Small built-in potential (006 eV) junction-induced exciton dissociation highly unlikely

CNTPolymer solar cells unlikely to work well with mixed CNT distribution

eg Kymakis E et al Rev Adv Mater Sci 2005 10 300

Y Kanai and JCG NanoLett (2008) 15

Using Computational Quantum Mechanics to Design New Mechanisms

What could this mean for a solar cell

VBM

CBM

Straight Wire

VBM

CBM

Tapered Wire

Axial charge redistribution due to quantum confinement

on

a on

Z Wu J B Neaton and JCG PRL 100 246804 (2008) Nanoletters (2009)

Majo po n a ad an ag no dop ng n d d

Electronhole13 charge13 separaampon13 through13 thermalizaampon

16

Amorphous Silicon PV The Slow Holes

08

06

04

02

0

-02

No dangling bonds Dangling bond

s

-6 -4 -2 0 2 Total energy (eV)

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

Ho

le t

rap

dep

th (

eV)

This type of bond switch can be as bad for hole mobility as

dangling bonds

17

Hole Traps Dangling Bond vs Strain

Dangling bond hole trap

Strain-only hole trap (stronger)

LK Wager and JCG Phys Rev Le 18

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

New Microscopic Picture of Holes in a-Si

Computational quantum mechanics shows that pressure can mitigate these traps

LK Wager and JCG Phys Rev Le 19

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

Another Thin-Film PV Example Hyper-Doped Silicon

bull Silicon doped with chalcogen atoms (sulfur selenium tellurium) to non-equilibrium concentrations

bull Strong optical response at photon energies where silicon is typically transparent

bull Promise as a photovoltaic material

copy source unknown All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairuse

20

Evolution of Density of States SiSe Si53Se1

Si431Se1 pure Silicon

21

Needing to Know Structure and Chemistry

Image of uantum vials in public domain Figure copy source unknown Data adapted from Physical Review B 60 no 2704 (1) All rightsreserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairuse 22

Needing to Know Structure and Chemistry

copy APS All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmitedufairuse

Clean Surface

Emits blue light

A Puzder et al Physical Review Letters 88 097401 (2002)

Oxygenated Cluster

Emits red light

23

rsha10
Rectangle
rsha10
Rectangle

Validation

How do we know when a simulation is right

24

Example Rayleigh-Taylor Instability

From Leo P KadanoffldquoThe Good the Bad and the Awful [ Scientific Simulation and Predictionrdquo

25

Example Rayleigh-Taylor Instability

bull Idea occurs anytime a dense heavy fluid is accelerated into a light fluid or lesser density

bull Slight perturbations to plane parallel interfaces are unstable hellipfingers grow into sets of intershypenetrating fingers

bull Observed in weather inversions salt domes star nebulae

bull How to model this process Requires solving hydrodynamics equations

26

Example Rayleigh-Taylor Instability

Photo sources copy PNAS (left) and IOP Publishing (right) All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

27

Example Rayleigh-Taylor Instability

Photo source copy IOP Publishing All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

28

Validation and Verification Verification and validation (VampV) are processes that help to ensure that models and simulations are correct and reliable

Verification ldquoDid I build the thing rightrdquo Have the model and simulation been built so that they fully satisfy the developerrsquos intent

Validation ldquoDid I build the right thingrdquo Will the model or simulation be able to adequately support its intended use Is its fidelity appropriate for that

after Dale Pace 29

Wersquove learned a lot

30

Some of the key remaining challenges

The electron correlation problem Only seriously affects a fraction of materials but that fraction tends to contain interesting physics and technological potential At this point there is no logical follow-up on LDAGGA

31

Some of the key remaining challenges

The time-scale problem Short (MD) and long (thermo) is not a problem Intermediate is a problem eg phase transformations

32

Some of the key remaining challenges

The knowledge problem To study an engineering propertybehavior with atomisticshyscale modeling one needs to understand somewhat what controls that property (in order to include the relevant boundary conditions)

eg If a property is controlled by impurities then intrinsic calculations will be irrelevant (Si conductivity)

33

Some of the key remaining challenges

The structure problem Even though the structure-property relation is a key tenet of Materials Science we have only very limited ability to predict structure (crystal structure amorphous microstructure )

34

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons

35

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons Courtesy of Patrick J Lynch License CC-BY

36

Computa(ons13 should13 not13 subs(tute13 for13 lack13 of13

knowledge

Computational modeling is very powerful but be smart

37

MIT OpenCourseWarehttpocwmitedu

3021J 1021J 10333J 18361J 2200J Introduction to Modelling and SimulationSpring 2012

For information about citing these materials or our Terms of Use visit httpocwmiteduterms

Page 9: Part II Lesson 11 A bit more solar PV, some verification & … · 2020. 1. 3. · 1.021, 3.021, 10.333, 22.00 : Introduction to Modeling and Simulation : Spring 2012. Part II –

Dye Sensitized Solar PV

Gratzel and OrsquoRegan (Nature 1991)

Made up of 3 active materials bullDye absorbs light bullTiO2 nanoparticles with very large surface area take electron

bullLiquid electrolyte delivers new electron from cathode to dye

Image in the public domain Via M R Jones on Wikimedia Commons

wwwenergyercom 9

Dye Sensitized Solar PV

bull Biggest problem is a liquid electrolyte

bull Relative energy levels of TiO2 and dye also key

copy source unknown All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

10

Going High Efciency Fundamenta imit

Conduction band

Excess energy above Eg heat

Eg

= max VOC

Valence band

n

As band gap increases the maximum open circuit voltage increases but the fracton of the solar spectrum absorbed decreases

11

copy DOE Lewis Group at Caltech All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfaq-fair-use

Multi-Junction Solar PV

Figures removed due to copyright restrictions See httpwwwnexpwcomtechnology tmhtml

bull Light Absorption

bull Band gaps

bull Conductivity Across Interfaces

bull Band gaps Band structures

12

Key Mechanism in Organic Solar PV Charge Separation at the Interface

Charge separation at this interface is highly efficient --Why

What is the detailed N S Sariciftci et al Science 1992 258 1474

B Kraabel et al JCP 1996 104 4268 mechanism for this picture C J Brabec et al CPL 2001 340 232

13

Excited State

Charge separated state essentially degenerate with bridge state

002 eV

13 eV

Bridge state forms hybridization of P3HT π state and C60 t1u state

P3HT C60

Y Kanai and JCG NanoLett 2007 14

CNTjP3HT Metallic CNT Carbon nanotubes instead of C60 Very little success thus far

Ef near P3HT bull Large charge transfer to the π state metallic CNT (~03 electron)

bull Fermi level just above P3HT HOMO state

Figures removed due to copyright restrictions See bull No interface states are formedFigures 4 and 5 in Nano Letters 8 no 3 (2008) so no Ef pinning

bull Small built-in potential (006 eV) junction-induced exciton dissociation highly unlikely

CNTPolymer solar cells unlikely to work well with mixed CNT distribution

eg Kymakis E et al Rev Adv Mater Sci 2005 10 300

Y Kanai and JCG NanoLett (2008) 15

Using Computational Quantum Mechanics to Design New Mechanisms

What could this mean for a solar cell

VBM

CBM

Straight Wire

VBM

CBM

Tapered Wire

Axial charge redistribution due to quantum confinement

on

a on

Z Wu J B Neaton and JCG PRL 100 246804 (2008) Nanoletters (2009)

Majo po n a ad an ag no dop ng n d d

Electronhole13 charge13 separaampon13 through13 thermalizaampon

16

Amorphous Silicon PV The Slow Holes

08

06

04

02

0

-02

No dangling bonds Dangling bond

s

-6 -4 -2 0 2 Total energy (eV)

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

Ho

le t

rap

dep

th (

eV)

This type of bond switch can be as bad for hole mobility as

dangling bonds

17

Hole Traps Dangling Bond vs Strain

Dangling bond hole trap

Strain-only hole trap (stronger)

LK Wager and JCG Phys Rev Le 18

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

New Microscopic Picture of Holes in a-Si

Computational quantum mechanics shows that pressure can mitigate these traps

LK Wager and JCG Phys Rev Le 19

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

Another Thin-Film PV Example Hyper-Doped Silicon

bull Silicon doped with chalcogen atoms (sulfur selenium tellurium) to non-equilibrium concentrations

bull Strong optical response at photon energies where silicon is typically transparent

bull Promise as a photovoltaic material

copy source unknown All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairuse

20

Evolution of Density of States SiSe Si53Se1

Si431Se1 pure Silicon

21

Needing to Know Structure and Chemistry

Image of uantum vials in public domain Figure copy source unknown Data adapted from Physical Review B 60 no 2704 (1) All rightsreserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairuse 22

Needing to Know Structure and Chemistry

copy APS All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmitedufairuse

Clean Surface

Emits blue light

A Puzder et al Physical Review Letters 88 097401 (2002)

Oxygenated Cluster

Emits red light

23

rsha10
Rectangle
rsha10
Rectangle

Validation

How do we know when a simulation is right

24

Example Rayleigh-Taylor Instability

From Leo P KadanoffldquoThe Good the Bad and the Awful [ Scientific Simulation and Predictionrdquo

25

Example Rayleigh-Taylor Instability

bull Idea occurs anytime a dense heavy fluid is accelerated into a light fluid or lesser density

bull Slight perturbations to plane parallel interfaces are unstable hellipfingers grow into sets of intershypenetrating fingers

bull Observed in weather inversions salt domes star nebulae

bull How to model this process Requires solving hydrodynamics equations

26

Example Rayleigh-Taylor Instability

Photo sources copy PNAS (left) and IOP Publishing (right) All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

27

Example Rayleigh-Taylor Instability

Photo source copy IOP Publishing All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

28

Validation and Verification Verification and validation (VampV) are processes that help to ensure that models and simulations are correct and reliable

Verification ldquoDid I build the thing rightrdquo Have the model and simulation been built so that they fully satisfy the developerrsquos intent

Validation ldquoDid I build the right thingrdquo Will the model or simulation be able to adequately support its intended use Is its fidelity appropriate for that

after Dale Pace 29

Wersquove learned a lot

30

Some of the key remaining challenges

The electron correlation problem Only seriously affects a fraction of materials but that fraction tends to contain interesting physics and technological potential At this point there is no logical follow-up on LDAGGA

31

Some of the key remaining challenges

The time-scale problem Short (MD) and long (thermo) is not a problem Intermediate is a problem eg phase transformations

32

Some of the key remaining challenges

The knowledge problem To study an engineering propertybehavior with atomisticshyscale modeling one needs to understand somewhat what controls that property (in order to include the relevant boundary conditions)

eg If a property is controlled by impurities then intrinsic calculations will be irrelevant (Si conductivity)

33

Some of the key remaining challenges

The structure problem Even though the structure-property relation is a key tenet of Materials Science we have only very limited ability to predict structure (crystal structure amorphous microstructure )

34

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons

35

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons Courtesy of Patrick J Lynch License CC-BY

36

Computa(ons13 should13 not13 subs(tute13 for13 lack13 of13

knowledge

Computational modeling is very powerful but be smart

37

MIT OpenCourseWarehttpocwmitedu

3021J 1021J 10333J 18361J 2200J Introduction to Modelling and SimulationSpring 2012

For information about citing these materials or our Terms of Use visit httpocwmiteduterms

Page 10: Part II Lesson 11 A bit more solar PV, some verification & … · 2020. 1. 3. · 1.021, 3.021, 10.333, 22.00 : Introduction to Modeling and Simulation : Spring 2012. Part II –

Dye Sensitized Solar PV

bull Biggest problem is a liquid electrolyte

bull Relative energy levels of TiO2 and dye also key

copy source unknown All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

10

Going High Efciency Fundamenta imit

Conduction band

Excess energy above Eg heat

Eg

= max VOC

Valence band

n

As band gap increases the maximum open circuit voltage increases but the fracton of the solar spectrum absorbed decreases

11

copy DOE Lewis Group at Caltech All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfaq-fair-use

Multi-Junction Solar PV

Figures removed due to copyright restrictions See httpwwwnexpwcomtechnology tmhtml

bull Light Absorption

bull Band gaps

bull Conductivity Across Interfaces

bull Band gaps Band structures

12

Key Mechanism in Organic Solar PV Charge Separation at the Interface

Charge separation at this interface is highly efficient --Why

What is the detailed N S Sariciftci et al Science 1992 258 1474

B Kraabel et al JCP 1996 104 4268 mechanism for this picture C J Brabec et al CPL 2001 340 232

13

Excited State

Charge separated state essentially degenerate with bridge state

002 eV

13 eV

Bridge state forms hybridization of P3HT π state and C60 t1u state

P3HT C60

Y Kanai and JCG NanoLett 2007 14

CNTjP3HT Metallic CNT Carbon nanotubes instead of C60 Very little success thus far

Ef near P3HT bull Large charge transfer to the π state metallic CNT (~03 electron)

bull Fermi level just above P3HT HOMO state

Figures removed due to copyright restrictions See bull No interface states are formedFigures 4 and 5 in Nano Letters 8 no 3 (2008) so no Ef pinning

bull Small built-in potential (006 eV) junction-induced exciton dissociation highly unlikely

CNTPolymer solar cells unlikely to work well with mixed CNT distribution

eg Kymakis E et al Rev Adv Mater Sci 2005 10 300

Y Kanai and JCG NanoLett (2008) 15

Using Computational Quantum Mechanics to Design New Mechanisms

What could this mean for a solar cell

VBM

CBM

Straight Wire

VBM

CBM

Tapered Wire

Axial charge redistribution due to quantum confinement

on

a on

Z Wu J B Neaton and JCG PRL 100 246804 (2008) Nanoletters (2009)

Majo po n a ad an ag no dop ng n d d

Electronhole13 charge13 separaampon13 through13 thermalizaampon

16

Amorphous Silicon PV The Slow Holes

08

06

04

02

0

-02

No dangling bonds Dangling bond

s

-6 -4 -2 0 2 Total energy (eV)

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

Ho

le t

rap

dep

th (

eV)

This type of bond switch can be as bad for hole mobility as

dangling bonds

17

Hole Traps Dangling Bond vs Strain

Dangling bond hole trap

Strain-only hole trap (stronger)

LK Wager and JCG Phys Rev Le 18

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

New Microscopic Picture of Holes in a-Si

Computational quantum mechanics shows that pressure can mitigate these traps

LK Wager and JCG Phys Rev Le 19

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

Another Thin-Film PV Example Hyper-Doped Silicon

bull Silicon doped with chalcogen atoms (sulfur selenium tellurium) to non-equilibrium concentrations

bull Strong optical response at photon energies where silicon is typically transparent

bull Promise as a photovoltaic material

copy source unknown All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairuse

20

Evolution of Density of States SiSe Si53Se1

Si431Se1 pure Silicon

21

Needing to Know Structure and Chemistry

Image of uantum vials in public domain Figure copy source unknown Data adapted from Physical Review B 60 no 2704 (1) All rightsreserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairuse 22

Needing to Know Structure and Chemistry

copy APS All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmitedufairuse

Clean Surface

Emits blue light

A Puzder et al Physical Review Letters 88 097401 (2002)

Oxygenated Cluster

Emits red light

23

rsha10
Rectangle
rsha10
Rectangle

Validation

How do we know when a simulation is right

24

Example Rayleigh-Taylor Instability

From Leo P KadanoffldquoThe Good the Bad and the Awful [ Scientific Simulation and Predictionrdquo

25

Example Rayleigh-Taylor Instability

bull Idea occurs anytime a dense heavy fluid is accelerated into a light fluid or lesser density

bull Slight perturbations to plane parallel interfaces are unstable hellipfingers grow into sets of intershypenetrating fingers

bull Observed in weather inversions salt domes star nebulae

bull How to model this process Requires solving hydrodynamics equations

26

Example Rayleigh-Taylor Instability

Photo sources copy PNAS (left) and IOP Publishing (right) All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

27

Example Rayleigh-Taylor Instability

Photo source copy IOP Publishing All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

28

Validation and Verification Verification and validation (VampV) are processes that help to ensure that models and simulations are correct and reliable

Verification ldquoDid I build the thing rightrdquo Have the model and simulation been built so that they fully satisfy the developerrsquos intent

Validation ldquoDid I build the right thingrdquo Will the model or simulation be able to adequately support its intended use Is its fidelity appropriate for that

after Dale Pace 29

Wersquove learned a lot

30

Some of the key remaining challenges

The electron correlation problem Only seriously affects a fraction of materials but that fraction tends to contain interesting physics and technological potential At this point there is no logical follow-up on LDAGGA

31

Some of the key remaining challenges

The time-scale problem Short (MD) and long (thermo) is not a problem Intermediate is a problem eg phase transformations

32

Some of the key remaining challenges

The knowledge problem To study an engineering propertybehavior with atomisticshyscale modeling one needs to understand somewhat what controls that property (in order to include the relevant boundary conditions)

eg If a property is controlled by impurities then intrinsic calculations will be irrelevant (Si conductivity)

33

Some of the key remaining challenges

The structure problem Even though the structure-property relation is a key tenet of Materials Science we have only very limited ability to predict structure (crystal structure amorphous microstructure )

34

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons

35

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons Courtesy of Patrick J Lynch License CC-BY

36

Computa(ons13 should13 not13 subs(tute13 for13 lack13 of13

knowledge

Computational modeling is very powerful but be smart

37

MIT OpenCourseWarehttpocwmitedu

3021J 1021J 10333J 18361J 2200J Introduction to Modelling and SimulationSpring 2012

For information about citing these materials or our Terms of Use visit httpocwmiteduterms

Page 11: Part II Lesson 11 A bit more solar PV, some verification & … · 2020. 1. 3. · 1.021, 3.021, 10.333, 22.00 : Introduction to Modeling and Simulation : Spring 2012. Part II –

Going High Efciency Fundamenta imit

Conduction band

Excess energy above Eg heat

Eg

= max VOC

Valence band

n

As band gap increases the maximum open circuit voltage increases but the fracton of the solar spectrum absorbed decreases

11

copy DOE Lewis Group at Caltech All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfaq-fair-use

Multi-Junction Solar PV

Figures removed due to copyright restrictions See httpwwwnexpwcomtechnology tmhtml

bull Light Absorption

bull Band gaps

bull Conductivity Across Interfaces

bull Band gaps Band structures

12

Key Mechanism in Organic Solar PV Charge Separation at the Interface

Charge separation at this interface is highly efficient --Why

What is the detailed N S Sariciftci et al Science 1992 258 1474

B Kraabel et al JCP 1996 104 4268 mechanism for this picture C J Brabec et al CPL 2001 340 232

13

Excited State

Charge separated state essentially degenerate with bridge state

002 eV

13 eV

Bridge state forms hybridization of P3HT π state and C60 t1u state

P3HT C60

Y Kanai and JCG NanoLett 2007 14

CNTjP3HT Metallic CNT Carbon nanotubes instead of C60 Very little success thus far

Ef near P3HT bull Large charge transfer to the π state metallic CNT (~03 electron)

bull Fermi level just above P3HT HOMO state

Figures removed due to copyright restrictions See bull No interface states are formedFigures 4 and 5 in Nano Letters 8 no 3 (2008) so no Ef pinning

bull Small built-in potential (006 eV) junction-induced exciton dissociation highly unlikely

CNTPolymer solar cells unlikely to work well with mixed CNT distribution

eg Kymakis E et al Rev Adv Mater Sci 2005 10 300

Y Kanai and JCG NanoLett (2008) 15

Using Computational Quantum Mechanics to Design New Mechanisms

What could this mean for a solar cell

VBM

CBM

Straight Wire

VBM

CBM

Tapered Wire

Axial charge redistribution due to quantum confinement

on

a on

Z Wu J B Neaton and JCG PRL 100 246804 (2008) Nanoletters (2009)

Majo po n a ad an ag no dop ng n d d

Electronhole13 charge13 separaampon13 through13 thermalizaampon

16

Amorphous Silicon PV The Slow Holes

08

06

04

02

0

-02

No dangling bonds Dangling bond

s

-6 -4 -2 0 2 Total energy (eV)

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

Ho

le t

rap

dep

th (

eV)

This type of bond switch can be as bad for hole mobility as

dangling bonds

17

Hole Traps Dangling Bond vs Strain

Dangling bond hole trap

Strain-only hole trap (stronger)

LK Wager and JCG Phys Rev Le 18

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

New Microscopic Picture of Holes in a-Si

Computational quantum mechanics shows that pressure can mitigate these traps

LK Wager and JCG Phys Rev Le 19

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

Another Thin-Film PV Example Hyper-Doped Silicon

bull Silicon doped with chalcogen atoms (sulfur selenium tellurium) to non-equilibrium concentrations

bull Strong optical response at photon energies where silicon is typically transparent

bull Promise as a photovoltaic material

copy source unknown All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairuse

20

Evolution of Density of States SiSe Si53Se1

Si431Se1 pure Silicon

21

Needing to Know Structure and Chemistry

Image of uantum vials in public domain Figure copy source unknown Data adapted from Physical Review B 60 no 2704 (1) All rightsreserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairuse 22

Needing to Know Structure and Chemistry

copy APS All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmitedufairuse

Clean Surface

Emits blue light

A Puzder et al Physical Review Letters 88 097401 (2002)

Oxygenated Cluster

Emits red light

23

rsha10
Rectangle
rsha10
Rectangle

Validation

How do we know when a simulation is right

24

Example Rayleigh-Taylor Instability

From Leo P KadanoffldquoThe Good the Bad and the Awful [ Scientific Simulation and Predictionrdquo

25

Example Rayleigh-Taylor Instability

bull Idea occurs anytime a dense heavy fluid is accelerated into a light fluid or lesser density

bull Slight perturbations to plane parallel interfaces are unstable hellipfingers grow into sets of intershypenetrating fingers

bull Observed in weather inversions salt domes star nebulae

bull How to model this process Requires solving hydrodynamics equations

26

Example Rayleigh-Taylor Instability

Photo sources copy PNAS (left) and IOP Publishing (right) All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

27

Example Rayleigh-Taylor Instability

Photo source copy IOP Publishing All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

28

Validation and Verification Verification and validation (VampV) are processes that help to ensure that models and simulations are correct and reliable

Verification ldquoDid I build the thing rightrdquo Have the model and simulation been built so that they fully satisfy the developerrsquos intent

Validation ldquoDid I build the right thingrdquo Will the model or simulation be able to adequately support its intended use Is its fidelity appropriate for that

after Dale Pace 29

Wersquove learned a lot

30

Some of the key remaining challenges

The electron correlation problem Only seriously affects a fraction of materials but that fraction tends to contain interesting physics and technological potential At this point there is no logical follow-up on LDAGGA

31

Some of the key remaining challenges

The time-scale problem Short (MD) and long (thermo) is not a problem Intermediate is a problem eg phase transformations

32

Some of the key remaining challenges

The knowledge problem To study an engineering propertybehavior with atomisticshyscale modeling one needs to understand somewhat what controls that property (in order to include the relevant boundary conditions)

eg If a property is controlled by impurities then intrinsic calculations will be irrelevant (Si conductivity)

33

Some of the key remaining challenges

The structure problem Even though the structure-property relation is a key tenet of Materials Science we have only very limited ability to predict structure (crystal structure amorphous microstructure )

34

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons

35

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons Courtesy of Patrick J Lynch License CC-BY

36

Computa(ons13 should13 not13 subs(tute13 for13 lack13 of13

knowledge

Computational modeling is very powerful but be smart

37

MIT OpenCourseWarehttpocwmitedu

3021J 1021J 10333J 18361J 2200J Introduction to Modelling and SimulationSpring 2012

For information about citing these materials or our Terms of Use visit httpocwmiteduterms

Page 12: Part II Lesson 11 A bit more solar PV, some verification & … · 2020. 1. 3. · 1.021, 3.021, 10.333, 22.00 : Introduction to Modeling and Simulation : Spring 2012. Part II –

Multi-Junction Solar PV

Figures removed due to copyright restrictions See httpwwwnexpwcomtechnology tmhtml

bull Light Absorption

bull Band gaps

bull Conductivity Across Interfaces

bull Band gaps Band structures

12

Key Mechanism in Organic Solar PV Charge Separation at the Interface

Charge separation at this interface is highly efficient --Why

What is the detailed N S Sariciftci et al Science 1992 258 1474

B Kraabel et al JCP 1996 104 4268 mechanism for this picture C J Brabec et al CPL 2001 340 232

13

Excited State

Charge separated state essentially degenerate with bridge state

002 eV

13 eV

Bridge state forms hybridization of P3HT π state and C60 t1u state

P3HT C60

Y Kanai and JCG NanoLett 2007 14

CNTjP3HT Metallic CNT Carbon nanotubes instead of C60 Very little success thus far

Ef near P3HT bull Large charge transfer to the π state metallic CNT (~03 electron)

bull Fermi level just above P3HT HOMO state

Figures removed due to copyright restrictions See bull No interface states are formedFigures 4 and 5 in Nano Letters 8 no 3 (2008) so no Ef pinning

bull Small built-in potential (006 eV) junction-induced exciton dissociation highly unlikely

CNTPolymer solar cells unlikely to work well with mixed CNT distribution

eg Kymakis E et al Rev Adv Mater Sci 2005 10 300

Y Kanai and JCG NanoLett (2008) 15

Using Computational Quantum Mechanics to Design New Mechanisms

What could this mean for a solar cell

VBM

CBM

Straight Wire

VBM

CBM

Tapered Wire

Axial charge redistribution due to quantum confinement

on

a on

Z Wu J B Neaton and JCG PRL 100 246804 (2008) Nanoletters (2009)

Majo po n a ad an ag no dop ng n d d

Electronhole13 charge13 separaampon13 through13 thermalizaampon

16

Amorphous Silicon PV The Slow Holes

08

06

04

02

0

-02

No dangling bonds Dangling bond

s

-6 -4 -2 0 2 Total energy (eV)

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

Ho

le t

rap

dep

th (

eV)

This type of bond switch can be as bad for hole mobility as

dangling bonds

17

Hole Traps Dangling Bond vs Strain

Dangling bond hole trap

Strain-only hole trap (stronger)

LK Wager and JCG Phys Rev Le 18

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

New Microscopic Picture of Holes in a-Si

Computational quantum mechanics shows that pressure can mitigate these traps

LK Wager and JCG Phys Rev Le 19

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

Another Thin-Film PV Example Hyper-Doped Silicon

bull Silicon doped with chalcogen atoms (sulfur selenium tellurium) to non-equilibrium concentrations

bull Strong optical response at photon energies where silicon is typically transparent

bull Promise as a photovoltaic material

copy source unknown All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairuse

20

Evolution of Density of States SiSe Si53Se1

Si431Se1 pure Silicon

21

Needing to Know Structure and Chemistry

Image of uantum vials in public domain Figure copy source unknown Data adapted from Physical Review B 60 no 2704 (1) All rightsreserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairuse 22

Needing to Know Structure and Chemistry

copy APS All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmitedufairuse

Clean Surface

Emits blue light

A Puzder et al Physical Review Letters 88 097401 (2002)

Oxygenated Cluster

Emits red light

23

rsha10
Rectangle
rsha10
Rectangle

Validation

How do we know when a simulation is right

24

Example Rayleigh-Taylor Instability

From Leo P KadanoffldquoThe Good the Bad and the Awful [ Scientific Simulation and Predictionrdquo

25

Example Rayleigh-Taylor Instability

bull Idea occurs anytime a dense heavy fluid is accelerated into a light fluid or lesser density

bull Slight perturbations to plane parallel interfaces are unstable hellipfingers grow into sets of intershypenetrating fingers

bull Observed in weather inversions salt domes star nebulae

bull How to model this process Requires solving hydrodynamics equations

26

Example Rayleigh-Taylor Instability

Photo sources copy PNAS (left) and IOP Publishing (right) All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

27

Example Rayleigh-Taylor Instability

Photo source copy IOP Publishing All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

28

Validation and Verification Verification and validation (VampV) are processes that help to ensure that models and simulations are correct and reliable

Verification ldquoDid I build the thing rightrdquo Have the model and simulation been built so that they fully satisfy the developerrsquos intent

Validation ldquoDid I build the right thingrdquo Will the model or simulation be able to adequately support its intended use Is its fidelity appropriate for that

after Dale Pace 29

Wersquove learned a lot

30

Some of the key remaining challenges

The electron correlation problem Only seriously affects a fraction of materials but that fraction tends to contain interesting physics and technological potential At this point there is no logical follow-up on LDAGGA

31

Some of the key remaining challenges

The time-scale problem Short (MD) and long (thermo) is not a problem Intermediate is a problem eg phase transformations

32

Some of the key remaining challenges

The knowledge problem To study an engineering propertybehavior with atomisticshyscale modeling one needs to understand somewhat what controls that property (in order to include the relevant boundary conditions)

eg If a property is controlled by impurities then intrinsic calculations will be irrelevant (Si conductivity)

33

Some of the key remaining challenges

The structure problem Even though the structure-property relation is a key tenet of Materials Science we have only very limited ability to predict structure (crystal structure amorphous microstructure )

34

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons

35

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons Courtesy of Patrick J Lynch License CC-BY

36

Computa(ons13 should13 not13 subs(tute13 for13 lack13 of13

knowledge

Computational modeling is very powerful but be smart

37

MIT OpenCourseWarehttpocwmitedu

3021J 1021J 10333J 18361J 2200J Introduction to Modelling and SimulationSpring 2012

For information about citing these materials or our Terms of Use visit httpocwmiteduterms

Page 13: Part II Lesson 11 A bit more solar PV, some verification & … · 2020. 1. 3. · 1.021, 3.021, 10.333, 22.00 : Introduction to Modeling and Simulation : Spring 2012. Part II –

Key Mechanism in Organic Solar PV Charge Separation at the Interface

Charge separation at this interface is highly efficient --Why

What is the detailed N S Sariciftci et al Science 1992 258 1474

B Kraabel et al JCP 1996 104 4268 mechanism for this picture C J Brabec et al CPL 2001 340 232

13

Excited State

Charge separated state essentially degenerate with bridge state

002 eV

13 eV

Bridge state forms hybridization of P3HT π state and C60 t1u state

P3HT C60

Y Kanai and JCG NanoLett 2007 14

CNTjP3HT Metallic CNT Carbon nanotubes instead of C60 Very little success thus far

Ef near P3HT bull Large charge transfer to the π state metallic CNT (~03 electron)

bull Fermi level just above P3HT HOMO state

Figures removed due to copyright restrictions See bull No interface states are formedFigures 4 and 5 in Nano Letters 8 no 3 (2008) so no Ef pinning

bull Small built-in potential (006 eV) junction-induced exciton dissociation highly unlikely

CNTPolymer solar cells unlikely to work well with mixed CNT distribution

eg Kymakis E et al Rev Adv Mater Sci 2005 10 300

Y Kanai and JCG NanoLett (2008) 15

Using Computational Quantum Mechanics to Design New Mechanisms

What could this mean for a solar cell

VBM

CBM

Straight Wire

VBM

CBM

Tapered Wire

Axial charge redistribution due to quantum confinement

on

a on

Z Wu J B Neaton and JCG PRL 100 246804 (2008) Nanoletters (2009)

Majo po n a ad an ag no dop ng n d d

Electronhole13 charge13 separaampon13 through13 thermalizaampon

16

Amorphous Silicon PV The Slow Holes

08

06

04

02

0

-02

No dangling bonds Dangling bond

s

-6 -4 -2 0 2 Total energy (eV)

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

Ho

le t

rap

dep

th (

eV)

This type of bond switch can be as bad for hole mobility as

dangling bonds

17

Hole Traps Dangling Bond vs Strain

Dangling bond hole trap

Strain-only hole trap (stronger)

LK Wager and JCG Phys Rev Le 18

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

New Microscopic Picture of Holes in a-Si

Computational quantum mechanics shows that pressure can mitigate these traps

LK Wager and JCG Phys Rev Le 19

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

Another Thin-Film PV Example Hyper-Doped Silicon

bull Silicon doped with chalcogen atoms (sulfur selenium tellurium) to non-equilibrium concentrations

bull Strong optical response at photon energies where silicon is typically transparent

bull Promise as a photovoltaic material

copy source unknown All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairuse

20

Evolution of Density of States SiSe Si53Se1

Si431Se1 pure Silicon

21

Needing to Know Structure and Chemistry

Image of uantum vials in public domain Figure copy source unknown Data adapted from Physical Review B 60 no 2704 (1) All rightsreserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairuse 22

Needing to Know Structure and Chemistry

copy APS All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmitedufairuse

Clean Surface

Emits blue light

A Puzder et al Physical Review Letters 88 097401 (2002)

Oxygenated Cluster

Emits red light

23

rsha10
Rectangle
rsha10
Rectangle

Validation

How do we know when a simulation is right

24

Example Rayleigh-Taylor Instability

From Leo P KadanoffldquoThe Good the Bad and the Awful [ Scientific Simulation and Predictionrdquo

25

Example Rayleigh-Taylor Instability

bull Idea occurs anytime a dense heavy fluid is accelerated into a light fluid or lesser density

bull Slight perturbations to plane parallel interfaces are unstable hellipfingers grow into sets of intershypenetrating fingers

bull Observed in weather inversions salt domes star nebulae

bull How to model this process Requires solving hydrodynamics equations

26

Example Rayleigh-Taylor Instability

Photo sources copy PNAS (left) and IOP Publishing (right) All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

27

Example Rayleigh-Taylor Instability

Photo source copy IOP Publishing All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

28

Validation and Verification Verification and validation (VampV) are processes that help to ensure that models and simulations are correct and reliable

Verification ldquoDid I build the thing rightrdquo Have the model and simulation been built so that they fully satisfy the developerrsquos intent

Validation ldquoDid I build the right thingrdquo Will the model or simulation be able to adequately support its intended use Is its fidelity appropriate for that

after Dale Pace 29

Wersquove learned a lot

30

Some of the key remaining challenges

The electron correlation problem Only seriously affects a fraction of materials but that fraction tends to contain interesting physics and technological potential At this point there is no logical follow-up on LDAGGA

31

Some of the key remaining challenges

The time-scale problem Short (MD) and long (thermo) is not a problem Intermediate is a problem eg phase transformations

32

Some of the key remaining challenges

The knowledge problem To study an engineering propertybehavior with atomisticshyscale modeling one needs to understand somewhat what controls that property (in order to include the relevant boundary conditions)

eg If a property is controlled by impurities then intrinsic calculations will be irrelevant (Si conductivity)

33

Some of the key remaining challenges

The structure problem Even though the structure-property relation is a key tenet of Materials Science we have only very limited ability to predict structure (crystal structure amorphous microstructure )

34

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons

35

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons Courtesy of Patrick J Lynch License CC-BY

36

Computa(ons13 should13 not13 subs(tute13 for13 lack13 of13

knowledge

Computational modeling is very powerful but be smart

37

MIT OpenCourseWarehttpocwmitedu

3021J 1021J 10333J 18361J 2200J Introduction to Modelling and SimulationSpring 2012

For information about citing these materials or our Terms of Use visit httpocwmiteduterms

Page 14: Part II Lesson 11 A bit more solar PV, some verification & … · 2020. 1. 3. · 1.021, 3.021, 10.333, 22.00 : Introduction to Modeling and Simulation : Spring 2012. Part II –

Excited State

Charge separated state essentially degenerate with bridge state

002 eV

13 eV

Bridge state forms hybridization of P3HT π state and C60 t1u state

P3HT C60

Y Kanai and JCG NanoLett 2007 14

CNTjP3HT Metallic CNT Carbon nanotubes instead of C60 Very little success thus far

Ef near P3HT bull Large charge transfer to the π state metallic CNT (~03 electron)

bull Fermi level just above P3HT HOMO state

Figures removed due to copyright restrictions See bull No interface states are formedFigures 4 and 5 in Nano Letters 8 no 3 (2008) so no Ef pinning

bull Small built-in potential (006 eV) junction-induced exciton dissociation highly unlikely

CNTPolymer solar cells unlikely to work well with mixed CNT distribution

eg Kymakis E et al Rev Adv Mater Sci 2005 10 300

Y Kanai and JCG NanoLett (2008) 15

Using Computational Quantum Mechanics to Design New Mechanisms

What could this mean for a solar cell

VBM

CBM

Straight Wire

VBM

CBM

Tapered Wire

Axial charge redistribution due to quantum confinement

on

a on

Z Wu J B Neaton and JCG PRL 100 246804 (2008) Nanoletters (2009)

Majo po n a ad an ag no dop ng n d d

Electronhole13 charge13 separaampon13 through13 thermalizaampon

16

Amorphous Silicon PV The Slow Holes

08

06

04

02

0

-02

No dangling bonds Dangling bond

s

-6 -4 -2 0 2 Total energy (eV)

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

Ho

le t

rap

dep

th (

eV)

This type of bond switch can be as bad for hole mobility as

dangling bonds

17

Hole Traps Dangling Bond vs Strain

Dangling bond hole trap

Strain-only hole trap (stronger)

LK Wager and JCG Phys Rev Le 18

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

New Microscopic Picture of Holes in a-Si

Computational quantum mechanics shows that pressure can mitigate these traps

LK Wager and JCG Phys Rev Le 19

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

Another Thin-Film PV Example Hyper-Doped Silicon

bull Silicon doped with chalcogen atoms (sulfur selenium tellurium) to non-equilibrium concentrations

bull Strong optical response at photon energies where silicon is typically transparent

bull Promise as a photovoltaic material

copy source unknown All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairuse

20

Evolution of Density of States SiSe Si53Se1

Si431Se1 pure Silicon

21

Needing to Know Structure and Chemistry

Image of uantum vials in public domain Figure copy source unknown Data adapted from Physical Review B 60 no 2704 (1) All rightsreserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairuse 22

Needing to Know Structure and Chemistry

copy APS All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmitedufairuse

Clean Surface

Emits blue light

A Puzder et al Physical Review Letters 88 097401 (2002)

Oxygenated Cluster

Emits red light

23

rsha10
Rectangle
rsha10
Rectangle

Validation

How do we know when a simulation is right

24

Example Rayleigh-Taylor Instability

From Leo P KadanoffldquoThe Good the Bad and the Awful [ Scientific Simulation and Predictionrdquo

25

Example Rayleigh-Taylor Instability

bull Idea occurs anytime a dense heavy fluid is accelerated into a light fluid or lesser density

bull Slight perturbations to plane parallel interfaces are unstable hellipfingers grow into sets of intershypenetrating fingers

bull Observed in weather inversions salt domes star nebulae

bull How to model this process Requires solving hydrodynamics equations

26

Example Rayleigh-Taylor Instability

Photo sources copy PNAS (left) and IOP Publishing (right) All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

27

Example Rayleigh-Taylor Instability

Photo source copy IOP Publishing All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

28

Validation and Verification Verification and validation (VampV) are processes that help to ensure that models and simulations are correct and reliable

Verification ldquoDid I build the thing rightrdquo Have the model and simulation been built so that they fully satisfy the developerrsquos intent

Validation ldquoDid I build the right thingrdquo Will the model or simulation be able to adequately support its intended use Is its fidelity appropriate for that

after Dale Pace 29

Wersquove learned a lot

30

Some of the key remaining challenges

The electron correlation problem Only seriously affects a fraction of materials but that fraction tends to contain interesting physics and technological potential At this point there is no logical follow-up on LDAGGA

31

Some of the key remaining challenges

The time-scale problem Short (MD) and long (thermo) is not a problem Intermediate is a problem eg phase transformations

32

Some of the key remaining challenges

The knowledge problem To study an engineering propertybehavior with atomisticshyscale modeling one needs to understand somewhat what controls that property (in order to include the relevant boundary conditions)

eg If a property is controlled by impurities then intrinsic calculations will be irrelevant (Si conductivity)

33

Some of the key remaining challenges

The structure problem Even though the structure-property relation is a key tenet of Materials Science we have only very limited ability to predict structure (crystal structure amorphous microstructure )

34

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons

35

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons Courtesy of Patrick J Lynch License CC-BY

36

Computa(ons13 should13 not13 subs(tute13 for13 lack13 of13

knowledge

Computational modeling is very powerful but be smart

37

MIT OpenCourseWarehttpocwmitedu

3021J 1021J 10333J 18361J 2200J Introduction to Modelling and SimulationSpring 2012

For information about citing these materials or our Terms of Use visit httpocwmiteduterms

Page 15: Part II Lesson 11 A bit more solar PV, some verification & … · 2020. 1. 3. · 1.021, 3.021, 10.333, 22.00 : Introduction to Modeling and Simulation : Spring 2012. Part II –

CNTjP3HT Metallic CNT Carbon nanotubes instead of C60 Very little success thus far

Ef near P3HT bull Large charge transfer to the π state metallic CNT (~03 electron)

bull Fermi level just above P3HT HOMO state

Figures removed due to copyright restrictions See bull No interface states are formedFigures 4 and 5 in Nano Letters 8 no 3 (2008) so no Ef pinning

bull Small built-in potential (006 eV) junction-induced exciton dissociation highly unlikely

CNTPolymer solar cells unlikely to work well with mixed CNT distribution

eg Kymakis E et al Rev Adv Mater Sci 2005 10 300

Y Kanai and JCG NanoLett (2008) 15

Using Computational Quantum Mechanics to Design New Mechanisms

What could this mean for a solar cell

VBM

CBM

Straight Wire

VBM

CBM

Tapered Wire

Axial charge redistribution due to quantum confinement

on

a on

Z Wu J B Neaton and JCG PRL 100 246804 (2008) Nanoletters (2009)

Majo po n a ad an ag no dop ng n d d

Electronhole13 charge13 separaampon13 through13 thermalizaampon

16

Amorphous Silicon PV The Slow Holes

08

06

04

02

0

-02

No dangling bonds Dangling bond

s

-6 -4 -2 0 2 Total energy (eV)

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

Ho

le t

rap

dep

th (

eV)

This type of bond switch can be as bad for hole mobility as

dangling bonds

17

Hole Traps Dangling Bond vs Strain

Dangling bond hole trap

Strain-only hole trap (stronger)

LK Wager and JCG Phys Rev Le 18

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

New Microscopic Picture of Holes in a-Si

Computational quantum mechanics shows that pressure can mitigate these traps

LK Wager and JCG Phys Rev Le 19

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

Another Thin-Film PV Example Hyper-Doped Silicon

bull Silicon doped with chalcogen atoms (sulfur selenium tellurium) to non-equilibrium concentrations

bull Strong optical response at photon energies where silicon is typically transparent

bull Promise as a photovoltaic material

copy source unknown All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairuse

20

Evolution of Density of States SiSe Si53Se1

Si431Se1 pure Silicon

21

Needing to Know Structure and Chemistry

Image of uantum vials in public domain Figure copy source unknown Data adapted from Physical Review B 60 no 2704 (1) All rightsreserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairuse 22

Needing to Know Structure and Chemistry

copy APS All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmitedufairuse

Clean Surface

Emits blue light

A Puzder et al Physical Review Letters 88 097401 (2002)

Oxygenated Cluster

Emits red light

23

rsha10
Rectangle
rsha10
Rectangle

Validation

How do we know when a simulation is right

24

Example Rayleigh-Taylor Instability

From Leo P KadanoffldquoThe Good the Bad and the Awful [ Scientific Simulation and Predictionrdquo

25

Example Rayleigh-Taylor Instability

bull Idea occurs anytime a dense heavy fluid is accelerated into a light fluid or lesser density

bull Slight perturbations to plane parallel interfaces are unstable hellipfingers grow into sets of intershypenetrating fingers

bull Observed in weather inversions salt domes star nebulae

bull How to model this process Requires solving hydrodynamics equations

26

Example Rayleigh-Taylor Instability

Photo sources copy PNAS (left) and IOP Publishing (right) All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

27

Example Rayleigh-Taylor Instability

Photo source copy IOP Publishing All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

28

Validation and Verification Verification and validation (VampV) are processes that help to ensure that models and simulations are correct and reliable

Verification ldquoDid I build the thing rightrdquo Have the model and simulation been built so that they fully satisfy the developerrsquos intent

Validation ldquoDid I build the right thingrdquo Will the model or simulation be able to adequately support its intended use Is its fidelity appropriate for that

after Dale Pace 29

Wersquove learned a lot

30

Some of the key remaining challenges

The electron correlation problem Only seriously affects a fraction of materials but that fraction tends to contain interesting physics and technological potential At this point there is no logical follow-up on LDAGGA

31

Some of the key remaining challenges

The time-scale problem Short (MD) and long (thermo) is not a problem Intermediate is a problem eg phase transformations

32

Some of the key remaining challenges

The knowledge problem To study an engineering propertybehavior with atomisticshyscale modeling one needs to understand somewhat what controls that property (in order to include the relevant boundary conditions)

eg If a property is controlled by impurities then intrinsic calculations will be irrelevant (Si conductivity)

33

Some of the key remaining challenges

The structure problem Even though the structure-property relation is a key tenet of Materials Science we have only very limited ability to predict structure (crystal structure amorphous microstructure )

34

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons

35

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons Courtesy of Patrick J Lynch License CC-BY

36

Computa(ons13 should13 not13 subs(tute13 for13 lack13 of13

knowledge

Computational modeling is very powerful but be smart

37

MIT OpenCourseWarehttpocwmitedu

3021J 1021J 10333J 18361J 2200J Introduction to Modelling and SimulationSpring 2012

For information about citing these materials or our Terms of Use visit httpocwmiteduterms

Page 16: Part II Lesson 11 A bit more solar PV, some verification & … · 2020. 1. 3. · 1.021, 3.021, 10.333, 22.00 : Introduction to Modeling and Simulation : Spring 2012. Part II –

Using Computational Quantum Mechanics to Design New Mechanisms

What could this mean for a solar cell

VBM

CBM

Straight Wire

VBM

CBM

Tapered Wire

Axial charge redistribution due to quantum confinement

on

a on

Z Wu J B Neaton and JCG PRL 100 246804 (2008) Nanoletters (2009)

Majo po n a ad an ag no dop ng n d d

Electronhole13 charge13 separaampon13 through13 thermalizaampon

16

Amorphous Silicon PV The Slow Holes

08

06

04

02

0

-02

No dangling bonds Dangling bond

s

-6 -4 -2 0 2 Total energy (eV)

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

Ho

le t

rap

dep

th (

eV)

This type of bond switch can be as bad for hole mobility as

dangling bonds

17

Hole Traps Dangling Bond vs Strain

Dangling bond hole trap

Strain-only hole trap (stronger)

LK Wager and JCG Phys Rev Le 18

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

New Microscopic Picture of Holes in a-Si

Computational quantum mechanics shows that pressure can mitigate these traps

LK Wager and JCG Phys Rev Le 19

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

Another Thin-Film PV Example Hyper-Doped Silicon

bull Silicon doped with chalcogen atoms (sulfur selenium tellurium) to non-equilibrium concentrations

bull Strong optical response at photon energies where silicon is typically transparent

bull Promise as a photovoltaic material

copy source unknown All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairuse

20

Evolution of Density of States SiSe Si53Se1

Si431Se1 pure Silicon

21

Needing to Know Structure and Chemistry

Image of uantum vials in public domain Figure copy source unknown Data adapted from Physical Review B 60 no 2704 (1) All rightsreserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairuse 22

Needing to Know Structure and Chemistry

copy APS All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmitedufairuse

Clean Surface

Emits blue light

A Puzder et al Physical Review Letters 88 097401 (2002)

Oxygenated Cluster

Emits red light

23

rsha10
Rectangle
rsha10
Rectangle

Validation

How do we know when a simulation is right

24

Example Rayleigh-Taylor Instability

From Leo P KadanoffldquoThe Good the Bad and the Awful [ Scientific Simulation and Predictionrdquo

25

Example Rayleigh-Taylor Instability

bull Idea occurs anytime a dense heavy fluid is accelerated into a light fluid or lesser density

bull Slight perturbations to plane parallel interfaces are unstable hellipfingers grow into sets of intershypenetrating fingers

bull Observed in weather inversions salt domes star nebulae

bull How to model this process Requires solving hydrodynamics equations

26

Example Rayleigh-Taylor Instability

Photo sources copy PNAS (left) and IOP Publishing (right) All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

27

Example Rayleigh-Taylor Instability

Photo source copy IOP Publishing All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

28

Validation and Verification Verification and validation (VampV) are processes that help to ensure that models and simulations are correct and reliable

Verification ldquoDid I build the thing rightrdquo Have the model and simulation been built so that they fully satisfy the developerrsquos intent

Validation ldquoDid I build the right thingrdquo Will the model or simulation be able to adequately support its intended use Is its fidelity appropriate for that

after Dale Pace 29

Wersquove learned a lot

30

Some of the key remaining challenges

The electron correlation problem Only seriously affects a fraction of materials but that fraction tends to contain interesting physics and technological potential At this point there is no logical follow-up on LDAGGA

31

Some of the key remaining challenges

The time-scale problem Short (MD) and long (thermo) is not a problem Intermediate is a problem eg phase transformations

32

Some of the key remaining challenges

The knowledge problem To study an engineering propertybehavior with atomisticshyscale modeling one needs to understand somewhat what controls that property (in order to include the relevant boundary conditions)

eg If a property is controlled by impurities then intrinsic calculations will be irrelevant (Si conductivity)

33

Some of the key remaining challenges

The structure problem Even though the structure-property relation is a key tenet of Materials Science we have only very limited ability to predict structure (crystal structure amorphous microstructure )

34

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons

35

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons Courtesy of Patrick J Lynch License CC-BY

36

Computa(ons13 should13 not13 subs(tute13 for13 lack13 of13

knowledge

Computational modeling is very powerful but be smart

37

MIT OpenCourseWarehttpocwmitedu

3021J 1021J 10333J 18361J 2200J Introduction to Modelling and SimulationSpring 2012

For information about citing these materials or our Terms of Use visit httpocwmiteduterms

Page 17: Part II Lesson 11 A bit more solar PV, some verification & … · 2020. 1. 3. · 1.021, 3.021, 10.333, 22.00 : Introduction to Modeling and Simulation : Spring 2012. Part II –

Amorphous Silicon PV The Slow Holes

08

06

04

02

0

-02

No dangling bonds Dangling bond

s

-6 -4 -2 0 2 Total energy (eV)

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

Ho

le t

rap

dep

th (

eV)

This type of bond switch can be as bad for hole mobility as

dangling bonds

17

Hole Traps Dangling Bond vs Strain

Dangling bond hole trap

Strain-only hole trap (stronger)

LK Wager and JCG Phys Rev Le 18

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

New Microscopic Picture of Holes in a-Si

Computational quantum mechanics shows that pressure can mitigate these traps

LK Wager and JCG Phys Rev Le 19

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

Another Thin-Film PV Example Hyper-Doped Silicon

bull Silicon doped with chalcogen atoms (sulfur selenium tellurium) to non-equilibrium concentrations

bull Strong optical response at photon energies where silicon is typically transparent

bull Promise as a photovoltaic material

copy source unknown All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairuse

20

Evolution of Density of States SiSe Si53Se1

Si431Se1 pure Silicon

21

Needing to Know Structure and Chemistry

Image of uantum vials in public domain Figure copy source unknown Data adapted from Physical Review B 60 no 2704 (1) All rightsreserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairuse 22

Needing to Know Structure and Chemistry

copy APS All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmitedufairuse

Clean Surface

Emits blue light

A Puzder et al Physical Review Letters 88 097401 (2002)

Oxygenated Cluster

Emits red light

23

rsha10
Rectangle
rsha10
Rectangle

Validation

How do we know when a simulation is right

24

Example Rayleigh-Taylor Instability

From Leo P KadanoffldquoThe Good the Bad and the Awful [ Scientific Simulation and Predictionrdquo

25

Example Rayleigh-Taylor Instability

bull Idea occurs anytime a dense heavy fluid is accelerated into a light fluid or lesser density

bull Slight perturbations to plane parallel interfaces are unstable hellipfingers grow into sets of intershypenetrating fingers

bull Observed in weather inversions salt domes star nebulae

bull How to model this process Requires solving hydrodynamics equations

26

Example Rayleigh-Taylor Instability

Photo sources copy PNAS (left) and IOP Publishing (right) All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

27

Example Rayleigh-Taylor Instability

Photo source copy IOP Publishing All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

28

Validation and Verification Verification and validation (VampV) are processes that help to ensure that models and simulations are correct and reliable

Verification ldquoDid I build the thing rightrdquo Have the model and simulation been built so that they fully satisfy the developerrsquos intent

Validation ldquoDid I build the right thingrdquo Will the model or simulation be able to adequately support its intended use Is its fidelity appropriate for that

after Dale Pace 29

Wersquove learned a lot

30

Some of the key remaining challenges

The electron correlation problem Only seriously affects a fraction of materials but that fraction tends to contain interesting physics and technological potential At this point there is no logical follow-up on LDAGGA

31

Some of the key remaining challenges

The time-scale problem Short (MD) and long (thermo) is not a problem Intermediate is a problem eg phase transformations

32

Some of the key remaining challenges

The knowledge problem To study an engineering propertybehavior with atomisticshyscale modeling one needs to understand somewhat what controls that property (in order to include the relevant boundary conditions)

eg If a property is controlled by impurities then intrinsic calculations will be irrelevant (Si conductivity)

33

Some of the key remaining challenges

The structure problem Even though the structure-property relation is a key tenet of Materials Science we have only very limited ability to predict structure (crystal structure amorphous microstructure )

34

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons

35

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons Courtesy of Patrick J Lynch License CC-BY

36

Computa(ons13 should13 not13 subs(tute13 for13 lack13 of13

knowledge

Computational modeling is very powerful but be smart

37

MIT OpenCourseWarehttpocwmitedu

3021J 1021J 10333J 18361J 2200J Introduction to Modelling and SimulationSpring 2012

For information about citing these materials or our Terms of Use visit httpocwmiteduterms

Page 18: Part II Lesson 11 A bit more solar PV, some verification & … · 2020. 1. 3. · 1.021, 3.021, 10.333, 22.00 : Introduction to Modeling and Simulation : Spring 2012. Part II –

Hole Traps Dangling Bond vs Strain

Dangling bond hole trap

Strain-only hole trap (stronger)

LK Wager and JCG Phys Rev Le 18

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

New Microscopic Picture of Holes in a-Si

Computational quantum mechanics shows that pressure can mitigate these traps

LK Wager and JCG Phys Rev Le 19

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

Another Thin-Film PV Example Hyper-Doped Silicon

bull Silicon doped with chalcogen atoms (sulfur selenium tellurium) to non-equilibrium concentrations

bull Strong optical response at photon energies where silicon is typically transparent

bull Promise as a photovoltaic material

copy source unknown All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairuse

20

Evolution of Density of States SiSe Si53Se1

Si431Se1 pure Silicon

21

Needing to Know Structure and Chemistry

Image of uantum vials in public domain Figure copy source unknown Data adapted from Physical Review B 60 no 2704 (1) All rightsreserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairuse 22

Needing to Know Structure and Chemistry

copy APS All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmitedufairuse

Clean Surface

Emits blue light

A Puzder et al Physical Review Letters 88 097401 (2002)

Oxygenated Cluster

Emits red light

23

rsha10
Rectangle
rsha10
Rectangle

Validation

How do we know when a simulation is right

24

Example Rayleigh-Taylor Instability

From Leo P KadanoffldquoThe Good the Bad and the Awful [ Scientific Simulation and Predictionrdquo

25

Example Rayleigh-Taylor Instability

bull Idea occurs anytime a dense heavy fluid is accelerated into a light fluid or lesser density

bull Slight perturbations to plane parallel interfaces are unstable hellipfingers grow into sets of intershypenetrating fingers

bull Observed in weather inversions salt domes star nebulae

bull How to model this process Requires solving hydrodynamics equations

26

Example Rayleigh-Taylor Instability

Photo sources copy PNAS (left) and IOP Publishing (right) All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

27

Example Rayleigh-Taylor Instability

Photo source copy IOP Publishing All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

28

Validation and Verification Verification and validation (VampV) are processes that help to ensure that models and simulations are correct and reliable

Verification ldquoDid I build the thing rightrdquo Have the model and simulation been built so that they fully satisfy the developerrsquos intent

Validation ldquoDid I build the right thingrdquo Will the model or simulation be able to adequately support its intended use Is its fidelity appropriate for that

after Dale Pace 29

Wersquove learned a lot

30

Some of the key remaining challenges

The electron correlation problem Only seriously affects a fraction of materials but that fraction tends to contain interesting physics and technological potential At this point there is no logical follow-up on LDAGGA

31

Some of the key remaining challenges

The time-scale problem Short (MD) and long (thermo) is not a problem Intermediate is a problem eg phase transformations

32

Some of the key remaining challenges

The knowledge problem To study an engineering propertybehavior with atomisticshyscale modeling one needs to understand somewhat what controls that property (in order to include the relevant boundary conditions)

eg If a property is controlled by impurities then intrinsic calculations will be irrelevant (Si conductivity)

33

Some of the key remaining challenges

The structure problem Even though the structure-property relation is a key tenet of Materials Science we have only very limited ability to predict structure (crystal structure amorphous microstructure )

34

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons

35

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons Courtesy of Patrick J Lynch License CC-BY

36

Computa(ons13 should13 not13 subs(tute13 for13 lack13 of13

knowledge

Computational modeling is very powerful but be smart

37

MIT OpenCourseWarehttpocwmitedu

3021J 1021J 10333J 18361J 2200J Introduction to Modelling and SimulationSpring 2012

For information about citing these materials or our Terms of Use visit httpocwmiteduterms

Page 19: Part II Lesson 11 A bit more solar PV, some verification & … · 2020. 1. 3. · 1.021, 3.021, 10.333, 22.00 : Introduction to Modeling and Simulation : Spring 2012. Part II –

New Microscopic Picture of Holes in a-Si

Computational quantum mechanics shows that pressure can mitigate these traps

LK Wager and JCG Phys Rev Le 19

copy APS Publishing All rights reserved See Wagner L and J C Grossman PhysicalReview Letters 101 no 265501 (2008) This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

Another Thin-Film PV Example Hyper-Doped Silicon

bull Silicon doped with chalcogen atoms (sulfur selenium tellurium) to non-equilibrium concentrations

bull Strong optical response at photon energies where silicon is typically transparent

bull Promise as a photovoltaic material

copy source unknown All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairuse

20

Evolution of Density of States SiSe Si53Se1

Si431Se1 pure Silicon

21

Needing to Know Structure and Chemistry

Image of uantum vials in public domain Figure copy source unknown Data adapted from Physical Review B 60 no 2704 (1) All rightsreserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairuse 22

Needing to Know Structure and Chemistry

copy APS All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmitedufairuse

Clean Surface

Emits blue light

A Puzder et al Physical Review Letters 88 097401 (2002)

Oxygenated Cluster

Emits red light

23

rsha10
Rectangle
rsha10
Rectangle

Validation

How do we know when a simulation is right

24

Example Rayleigh-Taylor Instability

From Leo P KadanoffldquoThe Good the Bad and the Awful [ Scientific Simulation and Predictionrdquo

25

Example Rayleigh-Taylor Instability

bull Idea occurs anytime a dense heavy fluid is accelerated into a light fluid or lesser density

bull Slight perturbations to plane parallel interfaces are unstable hellipfingers grow into sets of intershypenetrating fingers

bull Observed in weather inversions salt domes star nebulae

bull How to model this process Requires solving hydrodynamics equations

26

Example Rayleigh-Taylor Instability

Photo sources copy PNAS (left) and IOP Publishing (right) All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

27

Example Rayleigh-Taylor Instability

Photo source copy IOP Publishing All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

28

Validation and Verification Verification and validation (VampV) are processes that help to ensure that models and simulations are correct and reliable

Verification ldquoDid I build the thing rightrdquo Have the model and simulation been built so that they fully satisfy the developerrsquos intent

Validation ldquoDid I build the right thingrdquo Will the model or simulation be able to adequately support its intended use Is its fidelity appropriate for that

after Dale Pace 29

Wersquove learned a lot

30

Some of the key remaining challenges

The electron correlation problem Only seriously affects a fraction of materials but that fraction tends to contain interesting physics and technological potential At this point there is no logical follow-up on LDAGGA

31

Some of the key remaining challenges

The time-scale problem Short (MD) and long (thermo) is not a problem Intermediate is a problem eg phase transformations

32

Some of the key remaining challenges

The knowledge problem To study an engineering propertybehavior with atomisticshyscale modeling one needs to understand somewhat what controls that property (in order to include the relevant boundary conditions)

eg If a property is controlled by impurities then intrinsic calculations will be irrelevant (Si conductivity)

33

Some of the key remaining challenges

The structure problem Even though the structure-property relation is a key tenet of Materials Science we have only very limited ability to predict structure (crystal structure amorphous microstructure )

34

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons

35

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons Courtesy of Patrick J Lynch License CC-BY

36

Computa(ons13 should13 not13 subs(tute13 for13 lack13 of13

knowledge

Computational modeling is very powerful but be smart

37

MIT OpenCourseWarehttpocwmitedu

3021J 1021J 10333J 18361J 2200J Introduction to Modelling and SimulationSpring 2012

For information about citing these materials or our Terms of Use visit httpocwmiteduterms

Page 20: Part II Lesson 11 A bit more solar PV, some verification & … · 2020. 1. 3. · 1.021, 3.021, 10.333, 22.00 : Introduction to Modeling and Simulation : Spring 2012. Part II –

Another Thin-Film PV Example Hyper-Doped Silicon

bull Silicon doped with chalcogen atoms (sulfur selenium tellurium) to non-equilibrium concentrations

bull Strong optical response at photon energies where silicon is typically transparent

bull Promise as a photovoltaic material

copy source unknown All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairuse

20

Evolution of Density of States SiSe Si53Se1

Si431Se1 pure Silicon

21

Needing to Know Structure and Chemistry

Image of uantum vials in public domain Figure copy source unknown Data adapted from Physical Review B 60 no 2704 (1) All rightsreserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairuse 22

Needing to Know Structure and Chemistry

copy APS All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmitedufairuse

Clean Surface

Emits blue light

A Puzder et al Physical Review Letters 88 097401 (2002)

Oxygenated Cluster

Emits red light

23

rsha10
Rectangle
rsha10
Rectangle

Validation

How do we know when a simulation is right

24

Example Rayleigh-Taylor Instability

From Leo P KadanoffldquoThe Good the Bad and the Awful [ Scientific Simulation and Predictionrdquo

25

Example Rayleigh-Taylor Instability

bull Idea occurs anytime a dense heavy fluid is accelerated into a light fluid or lesser density

bull Slight perturbations to plane parallel interfaces are unstable hellipfingers grow into sets of intershypenetrating fingers

bull Observed in weather inversions salt domes star nebulae

bull How to model this process Requires solving hydrodynamics equations

26

Example Rayleigh-Taylor Instability

Photo sources copy PNAS (left) and IOP Publishing (right) All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

27

Example Rayleigh-Taylor Instability

Photo source copy IOP Publishing All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

28

Validation and Verification Verification and validation (VampV) are processes that help to ensure that models and simulations are correct and reliable

Verification ldquoDid I build the thing rightrdquo Have the model and simulation been built so that they fully satisfy the developerrsquos intent

Validation ldquoDid I build the right thingrdquo Will the model or simulation be able to adequately support its intended use Is its fidelity appropriate for that

after Dale Pace 29

Wersquove learned a lot

30

Some of the key remaining challenges

The electron correlation problem Only seriously affects a fraction of materials but that fraction tends to contain interesting physics and technological potential At this point there is no logical follow-up on LDAGGA

31

Some of the key remaining challenges

The time-scale problem Short (MD) and long (thermo) is not a problem Intermediate is a problem eg phase transformations

32

Some of the key remaining challenges

The knowledge problem To study an engineering propertybehavior with atomisticshyscale modeling one needs to understand somewhat what controls that property (in order to include the relevant boundary conditions)

eg If a property is controlled by impurities then intrinsic calculations will be irrelevant (Si conductivity)

33

Some of the key remaining challenges

The structure problem Even though the structure-property relation is a key tenet of Materials Science we have only very limited ability to predict structure (crystal structure amorphous microstructure )

34

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons

35

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons Courtesy of Patrick J Lynch License CC-BY

36

Computa(ons13 should13 not13 subs(tute13 for13 lack13 of13

knowledge

Computational modeling is very powerful but be smart

37

MIT OpenCourseWarehttpocwmitedu

3021J 1021J 10333J 18361J 2200J Introduction to Modelling and SimulationSpring 2012

For information about citing these materials or our Terms of Use visit httpocwmiteduterms

Page 21: Part II Lesson 11 A bit more solar PV, some verification & … · 2020. 1. 3. · 1.021, 3.021, 10.333, 22.00 : Introduction to Modeling and Simulation : Spring 2012. Part II –

Evolution of Density of States SiSe Si53Se1

Si431Se1 pure Silicon

21

Needing to Know Structure and Chemistry

Image of uantum vials in public domain Figure copy source unknown Data adapted from Physical Review B 60 no 2704 (1) All rightsreserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairuse 22

Needing to Know Structure and Chemistry

copy APS All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmitedufairuse

Clean Surface

Emits blue light

A Puzder et al Physical Review Letters 88 097401 (2002)

Oxygenated Cluster

Emits red light

23

rsha10
Rectangle
rsha10
Rectangle

Validation

How do we know when a simulation is right

24

Example Rayleigh-Taylor Instability

From Leo P KadanoffldquoThe Good the Bad and the Awful [ Scientific Simulation and Predictionrdquo

25

Example Rayleigh-Taylor Instability

bull Idea occurs anytime a dense heavy fluid is accelerated into a light fluid or lesser density

bull Slight perturbations to plane parallel interfaces are unstable hellipfingers grow into sets of intershypenetrating fingers

bull Observed in weather inversions salt domes star nebulae

bull How to model this process Requires solving hydrodynamics equations

26

Example Rayleigh-Taylor Instability

Photo sources copy PNAS (left) and IOP Publishing (right) All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

27

Example Rayleigh-Taylor Instability

Photo source copy IOP Publishing All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

28

Validation and Verification Verification and validation (VampV) are processes that help to ensure that models and simulations are correct and reliable

Verification ldquoDid I build the thing rightrdquo Have the model and simulation been built so that they fully satisfy the developerrsquos intent

Validation ldquoDid I build the right thingrdquo Will the model or simulation be able to adequately support its intended use Is its fidelity appropriate for that

after Dale Pace 29

Wersquove learned a lot

30

Some of the key remaining challenges

The electron correlation problem Only seriously affects a fraction of materials but that fraction tends to contain interesting physics and technological potential At this point there is no logical follow-up on LDAGGA

31

Some of the key remaining challenges

The time-scale problem Short (MD) and long (thermo) is not a problem Intermediate is a problem eg phase transformations

32

Some of the key remaining challenges

The knowledge problem To study an engineering propertybehavior with atomisticshyscale modeling one needs to understand somewhat what controls that property (in order to include the relevant boundary conditions)

eg If a property is controlled by impurities then intrinsic calculations will be irrelevant (Si conductivity)

33

Some of the key remaining challenges

The structure problem Even though the structure-property relation is a key tenet of Materials Science we have only very limited ability to predict structure (crystal structure amorphous microstructure )

34

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons

35

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons Courtesy of Patrick J Lynch License CC-BY

36

Computa(ons13 should13 not13 subs(tute13 for13 lack13 of13

knowledge

Computational modeling is very powerful but be smart

37

MIT OpenCourseWarehttpocwmitedu

3021J 1021J 10333J 18361J 2200J Introduction to Modelling and SimulationSpring 2012

For information about citing these materials or our Terms of Use visit httpocwmiteduterms

Page 22: Part II Lesson 11 A bit more solar PV, some verification & … · 2020. 1. 3. · 1.021, 3.021, 10.333, 22.00 : Introduction to Modeling and Simulation : Spring 2012. Part II –

Needing to Know Structure and Chemistry

Image of uantum vials in public domain Figure copy source unknown Data adapted from Physical Review B 60 no 2704 (1) All rightsreserved This content is excluded from our Creative Commons license For more information see httpocwmiteduhelpfafairuse 22

Needing to Know Structure and Chemistry

copy APS All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmitedufairuse

Clean Surface

Emits blue light

A Puzder et al Physical Review Letters 88 097401 (2002)

Oxygenated Cluster

Emits red light

23

rsha10
Rectangle
rsha10
Rectangle

Validation

How do we know when a simulation is right

24

Example Rayleigh-Taylor Instability

From Leo P KadanoffldquoThe Good the Bad and the Awful [ Scientific Simulation and Predictionrdquo

25

Example Rayleigh-Taylor Instability

bull Idea occurs anytime a dense heavy fluid is accelerated into a light fluid or lesser density

bull Slight perturbations to plane parallel interfaces are unstable hellipfingers grow into sets of intershypenetrating fingers

bull Observed in weather inversions salt domes star nebulae

bull How to model this process Requires solving hydrodynamics equations

26

Example Rayleigh-Taylor Instability

Photo sources copy PNAS (left) and IOP Publishing (right) All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

27

Example Rayleigh-Taylor Instability

Photo source copy IOP Publishing All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

28

Validation and Verification Verification and validation (VampV) are processes that help to ensure that models and simulations are correct and reliable

Verification ldquoDid I build the thing rightrdquo Have the model and simulation been built so that they fully satisfy the developerrsquos intent

Validation ldquoDid I build the right thingrdquo Will the model or simulation be able to adequately support its intended use Is its fidelity appropriate for that

after Dale Pace 29

Wersquove learned a lot

30

Some of the key remaining challenges

The electron correlation problem Only seriously affects a fraction of materials but that fraction tends to contain interesting physics and technological potential At this point there is no logical follow-up on LDAGGA

31

Some of the key remaining challenges

The time-scale problem Short (MD) and long (thermo) is not a problem Intermediate is a problem eg phase transformations

32

Some of the key remaining challenges

The knowledge problem To study an engineering propertybehavior with atomisticshyscale modeling one needs to understand somewhat what controls that property (in order to include the relevant boundary conditions)

eg If a property is controlled by impurities then intrinsic calculations will be irrelevant (Si conductivity)

33

Some of the key remaining challenges

The structure problem Even though the structure-property relation is a key tenet of Materials Science we have only very limited ability to predict structure (crystal structure amorphous microstructure )

34

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons

35

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons Courtesy of Patrick J Lynch License CC-BY

36

Computa(ons13 should13 not13 subs(tute13 for13 lack13 of13

knowledge

Computational modeling is very powerful but be smart

37

MIT OpenCourseWarehttpocwmitedu

3021J 1021J 10333J 18361J 2200J Introduction to Modelling and SimulationSpring 2012

For information about citing these materials or our Terms of Use visit httpocwmiteduterms

Page 23: Part II Lesson 11 A bit more solar PV, some verification & … · 2020. 1. 3. · 1.021, 3.021, 10.333, 22.00 : Introduction to Modeling and Simulation : Spring 2012. Part II –

Needing to Know Structure and Chemistry

copy APS All rights reserved This content is excluded from our Creative Commons license For more information see httpocwmitedufairuse

Clean Surface

Emits blue light

A Puzder et al Physical Review Letters 88 097401 (2002)

Oxygenated Cluster

Emits red light

23

rsha10
Rectangle
rsha10
Rectangle

Validation

How do we know when a simulation is right

24

Example Rayleigh-Taylor Instability

From Leo P KadanoffldquoThe Good the Bad and the Awful [ Scientific Simulation and Predictionrdquo

25

Example Rayleigh-Taylor Instability

bull Idea occurs anytime a dense heavy fluid is accelerated into a light fluid or lesser density

bull Slight perturbations to plane parallel interfaces are unstable hellipfingers grow into sets of intershypenetrating fingers

bull Observed in weather inversions salt domes star nebulae

bull How to model this process Requires solving hydrodynamics equations

26

Example Rayleigh-Taylor Instability

Photo sources copy PNAS (left) and IOP Publishing (right) All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

27

Example Rayleigh-Taylor Instability

Photo source copy IOP Publishing All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

28

Validation and Verification Verification and validation (VampV) are processes that help to ensure that models and simulations are correct and reliable

Verification ldquoDid I build the thing rightrdquo Have the model and simulation been built so that they fully satisfy the developerrsquos intent

Validation ldquoDid I build the right thingrdquo Will the model or simulation be able to adequately support its intended use Is its fidelity appropriate for that

after Dale Pace 29

Wersquove learned a lot

30

Some of the key remaining challenges

The electron correlation problem Only seriously affects a fraction of materials but that fraction tends to contain interesting physics and technological potential At this point there is no logical follow-up on LDAGGA

31

Some of the key remaining challenges

The time-scale problem Short (MD) and long (thermo) is not a problem Intermediate is a problem eg phase transformations

32

Some of the key remaining challenges

The knowledge problem To study an engineering propertybehavior with atomisticshyscale modeling one needs to understand somewhat what controls that property (in order to include the relevant boundary conditions)

eg If a property is controlled by impurities then intrinsic calculations will be irrelevant (Si conductivity)

33

Some of the key remaining challenges

The structure problem Even though the structure-property relation is a key tenet of Materials Science we have only very limited ability to predict structure (crystal structure amorphous microstructure )

34

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons

35

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons Courtesy of Patrick J Lynch License CC-BY

36

Computa(ons13 should13 not13 subs(tute13 for13 lack13 of13

knowledge

Computational modeling is very powerful but be smart

37

MIT OpenCourseWarehttpocwmitedu

3021J 1021J 10333J 18361J 2200J Introduction to Modelling and SimulationSpring 2012

For information about citing these materials or our Terms of Use visit httpocwmiteduterms

Page 24: Part II Lesson 11 A bit more solar PV, some verification & … · 2020. 1. 3. · 1.021, 3.021, 10.333, 22.00 : Introduction to Modeling and Simulation : Spring 2012. Part II –

Validation

How do we know when a simulation is right

24

Example Rayleigh-Taylor Instability

From Leo P KadanoffldquoThe Good the Bad and the Awful [ Scientific Simulation and Predictionrdquo

25

Example Rayleigh-Taylor Instability

bull Idea occurs anytime a dense heavy fluid is accelerated into a light fluid or lesser density

bull Slight perturbations to plane parallel interfaces are unstable hellipfingers grow into sets of intershypenetrating fingers

bull Observed in weather inversions salt domes star nebulae

bull How to model this process Requires solving hydrodynamics equations

26

Example Rayleigh-Taylor Instability

Photo sources copy PNAS (left) and IOP Publishing (right) All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

27

Example Rayleigh-Taylor Instability

Photo source copy IOP Publishing All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

28

Validation and Verification Verification and validation (VampV) are processes that help to ensure that models and simulations are correct and reliable

Verification ldquoDid I build the thing rightrdquo Have the model and simulation been built so that they fully satisfy the developerrsquos intent

Validation ldquoDid I build the right thingrdquo Will the model or simulation be able to adequately support its intended use Is its fidelity appropriate for that

after Dale Pace 29

Wersquove learned a lot

30

Some of the key remaining challenges

The electron correlation problem Only seriously affects a fraction of materials but that fraction tends to contain interesting physics and technological potential At this point there is no logical follow-up on LDAGGA

31

Some of the key remaining challenges

The time-scale problem Short (MD) and long (thermo) is not a problem Intermediate is a problem eg phase transformations

32

Some of the key remaining challenges

The knowledge problem To study an engineering propertybehavior with atomisticshyscale modeling one needs to understand somewhat what controls that property (in order to include the relevant boundary conditions)

eg If a property is controlled by impurities then intrinsic calculations will be irrelevant (Si conductivity)

33

Some of the key remaining challenges

The structure problem Even though the structure-property relation is a key tenet of Materials Science we have only very limited ability to predict structure (crystal structure amorphous microstructure )

34

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons

35

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons Courtesy of Patrick J Lynch License CC-BY

36

Computa(ons13 should13 not13 subs(tute13 for13 lack13 of13

knowledge

Computational modeling is very powerful but be smart

37

MIT OpenCourseWarehttpocwmitedu

3021J 1021J 10333J 18361J 2200J Introduction to Modelling and SimulationSpring 2012

For information about citing these materials or our Terms of Use visit httpocwmiteduterms

Page 25: Part II Lesson 11 A bit more solar PV, some verification & … · 2020. 1. 3. · 1.021, 3.021, 10.333, 22.00 : Introduction to Modeling and Simulation : Spring 2012. Part II –

Example Rayleigh-Taylor Instability

From Leo P KadanoffldquoThe Good the Bad and the Awful [ Scientific Simulation and Predictionrdquo

25

Example Rayleigh-Taylor Instability

bull Idea occurs anytime a dense heavy fluid is accelerated into a light fluid or lesser density

bull Slight perturbations to plane parallel interfaces are unstable hellipfingers grow into sets of intershypenetrating fingers

bull Observed in weather inversions salt domes star nebulae

bull How to model this process Requires solving hydrodynamics equations

26

Example Rayleigh-Taylor Instability

Photo sources copy PNAS (left) and IOP Publishing (right) All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

27

Example Rayleigh-Taylor Instability

Photo source copy IOP Publishing All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

28

Validation and Verification Verification and validation (VampV) are processes that help to ensure that models and simulations are correct and reliable

Verification ldquoDid I build the thing rightrdquo Have the model and simulation been built so that they fully satisfy the developerrsquos intent

Validation ldquoDid I build the right thingrdquo Will the model or simulation be able to adequately support its intended use Is its fidelity appropriate for that

after Dale Pace 29

Wersquove learned a lot

30

Some of the key remaining challenges

The electron correlation problem Only seriously affects a fraction of materials but that fraction tends to contain interesting physics and technological potential At this point there is no logical follow-up on LDAGGA

31

Some of the key remaining challenges

The time-scale problem Short (MD) and long (thermo) is not a problem Intermediate is a problem eg phase transformations

32

Some of the key remaining challenges

The knowledge problem To study an engineering propertybehavior with atomisticshyscale modeling one needs to understand somewhat what controls that property (in order to include the relevant boundary conditions)

eg If a property is controlled by impurities then intrinsic calculations will be irrelevant (Si conductivity)

33

Some of the key remaining challenges

The structure problem Even though the structure-property relation is a key tenet of Materials Science we have only very limited ability to predict structure (crystal structure amorphous microstructure )

34

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons

35

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons Courtesy of Patrick J Lynch License CC-BY

36

Computa(ons13 should13 not13 subs(tute13 for13 lack13 of13

knowledge

Computational modeling is very powerful but be smart

37

MIT OpenCourseWarehttpocwmitedu

3021J 1021J 10333J 18361J 2200J Introduction to Modelling and SimulationSpring 2012

For information about citing these materials or our Terms of Use visit httpocwmiteduterms

Page 26: Part II Lesson 11 A bit more solar PV, some verification & … · 2020. 1. 3. · 1.021, 3.021, 10.333, 22.00 : Introduction to Modeling and Simulation : Spring 2012. Part II –

Example Rayleigh-Taylor Instability

bull Idea occurs anytime a dense heavy fluid is accelerated into a light fluid or lesser density

bull Slight perturbations to plane parallel interfaces are unstable hellipfingers grow into sets of intershypenetrating fingers

bull Observed in weather inversions salt domes star nebulae

bull How to model this process Requires solving hydrodynamics equations

26

Example Rayleigh-Taylor Instability

Photo sources copy PNAS (left) and IOP Publishing (right) All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

27

Example Rayleigh-Taylor Instability

Photo source copy IOP Publishing All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

28

Validation and Verification Verification and validation (VampV) are processes that help to ensure that models and simulations are correct and reliable

Verification ldquoDid I build the thing rightrdquo Have the model and simulation been built so that they fully satisfy the developerrsquos intent

Validation ldquoDid I build the right thingrdquo Will the model or simulation be able to adequately support its intended use Is its fidelity appropriate for that

after Dale Pace 29

Wersquove learned a lot

30

Some of the key remaining challenges

The electron correlation problem Only seriously affects a fraction of materials but that fraction tends to contain interesting physics and technological potential At this point there is no logical follow-up on LDAGGA

31

Some of the key remaining challenges

The time-scale problem Short (MD) and long (thermo) is not a problem Intermediate is a problem eg phase transformations

32

Some of the key remaining challenges

The knowledge problem To study an engineering propertybehavior with atomisticshyscale modeling one needs to understand somewhat what controls that property (in order to include the relevant boundary conditions)

eg If a property is controlled by impurities then intrinsic calculations will be irrelevant (Si conductivity)

33

Some of the key remaining challenges

The structure problem Even though the structure-property relation is a key tenet of Materials Science we have only very limited ability to predict structure (crystal structure amorphous microstructure )

34

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons

35

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons Courtesy of Patrick J Lynch License CC-BY

36

Computa(ons13 should13 not13 subs(tute13 for13 lack13 of13

knowledge

Computational modeling is very powerful but be smart

37

MIT OpenCourseWarehttpocwmitedu

3021J 1021J 10333J 18361J 2200J Introduction to Modelling and SimulationSpring 2012

For information about citing these materials or our Terms of Use visit httpocwmiteduterms

Page 27: Part II Lesson 11 A bit more solar PV, some verification & … · 2020. 1. 3. · 1.021, 3.021, 10.333, 22.00 : Introduction to Modeling and Simulation : Spring 2012. Part II –

Example Rayleigh-Taylor Instability

Photo sources copy PNAS (left) and IOP Publishing (right) All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

27

Example Rayleigh-Taylor Instability

Photo source copy IOP Publishing All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

28

Validation and Verification Verification and validation (VampV) are processes that help to ensure that models and simulations are correct and reliable

Verification ldquoDid I build the thing rightrdquo Have the model and simulation been built so that they fully satisfy the developerrsquos intent

Validation ldquoDid I build the right thingrdquo Will the model or simulation be able to adequately support its intended use Is its fidelity appropriate for that

after Dale Pace 29

Wersquove learned a lot

30

Some of the key remaining challenges

The electron correlation problem Only seriously affects a fraction of materials but that fraction tends to contain interesting physics and technological potential At this point there is no logical follow-up on LDAGGA

31

Some of the key remaining challenges

The time-scale problem Short (MD) and long (thermo) is not a problem Intermediate is a problem eg phase transformations

32

Some of the key remaining challenges

The knowledge problem To study an engineering propertybehavior with atomisticshyscale modeling one needs to understand somewhat what controls that property (in order to include the relevant boundary conditions)

eg If a property is controlled by impurities then intrinsic calculations will be irrelevant (Si conductivity)

33

Some of the key remaining challenges

The structure problem Even though the structure-property relation is a key tenet of Materials Science we have only very limited ability to predict structure (crystal structure amorphous microstructure )

34

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons

35

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons Courtesy of Patrick J Lynch License CC-BY

36

Computa(ons13 should13 not13 subs(tute13 for13 lack13 of13

knowledge

Computational modeling is very powerful but be smart

37

MIT OpenCourseWarehttpocwmitedu

3021J 1021J 10333J 18361J 2200J Introduction to Modelling and SimulationSpring 2012

For information about citing these materials or our Terms of Use visit httpocwmiteduterms

Page 28: Part II Lesson 11 A bit more solar PV, some verification & … · 2020. 1. 3. · 1.021, 3.021, 10.333, 22.00 : Introduction to Modeling and Simulation : Spring 2012. Part II –

Example Rayleigh-Taylor Instability

Photo source copy IOP Publishing All rights reserved This content is excluded from our CreativeCommons license For more information see httpocwmiteduhelpfafairuse

28

Validation and Verification Verification and validation (VampV) are processes that help to ensure that models and simulations are correct and reliable

Verification ldquoDid I build the thing rightrdquo Have the model and simulation been built so that they fully satisfy the developerrsquos intent

Validation ldquoDid I build the right thingrdquo Will the model or simulation be able to adequately support its intended use Is its fidelity appropriate for that

after Dale Pace 29

Wersquove learned a lot

30

Some of the key remaining challenges

The electron correlation problem Only seriously affects a fraction of materials but that fraction tends to contain interesting physics and technological potential At this point there is no logical follow-up on LDAGGA

31

Some of the key remaining challenges

The time-scale problem Short (MD) and long (thermo) is not a problem Intermediate is a problem eg phase transformations

32

Some of the key remaining challenges

The knowledge problem To study an engineering propertybehavior with atomisticshyscale modeling one needs to understand somewhat what controls that property (in order to include the relevant boundary conditions)

eg If a property is controlled by impurities then intrinsic calculations will be irrelevant (Si conductivity)

33

Some of the key remaining challenges

The structure problem Even though the structure-property relation is a key tenet of Materials Science we have only very limited ability to predict structure (crystal structure amorphous microstructure )

34

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons

35

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons Courtesy of Patrick J Lynch License CC-BY

36

Computa(ons13 should13 not13 subs(tute13 for13 lack13 of13

knowledge

Computational modeling is very powerful but be smart

37

MIT OpenCourseWarehttpocwmitedu

3021J 1021J 10333J 18361J 2200J Introduction to Modelling and SimulationSpring 2012

For information about citing these materials or our Terms of Use visit httpocwmiteduterms

Page 29: Part II Lesson 11 A bit more solar PV, some verification & … · 2020. 1. 3. · 1.021, 3.021, 10.333, 22.00 : Introduction to Modeling and Simulation : Spring 2012. Part II –

Validation and Verification Verification and validation (VampV) are processes that help to ensure that models and simulations are correct and reliable

Verification ldquoDid I build the thing rightrdquo Have the model and simulation been built so that they fully satisfy the developerrsquos intent

Validation ldquoDid I build the right thingrdquo Will the model or simulation be able to adequately support its intended use Is its fidelity appropriate for that

after Dale Pace 29

Wersquove learned a lot

30

Some of the key remaining challenges

The electron correlation problem Only seriously affects a fraction of materials but that fraction tends to contain interesting physics and technological potential At this point there is no logical follow-up on LDAGGA

31

Some of the key remaining challenges

The time-scale problem Short (MD) and long (thermo) is not a problem Intermediate is a problem eg phase transformations

32

Some of the key remaining challenges

The knowledge problem To study an engineering propertybehavior with atomisticshyscale modeling one needs to understand somewhat what controls that property (in order to include the relevant boundary conditions)

eg If a property is controlled by impurities then intrinsic calculations will be irrelevant (Si conductivity)

33

Some of the key remaining challenges

The structure problem Even though the structure-property relation is a key tenet of Materials Science we have only very limited ability to predict structure (crystal structure amorphous microstructure )

34

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons

35

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons Courtesy of Patrick J Lynch License CC-BY

36

Computa(ons13 should13 not13 subs(tute13 for13 lack13 of13

knowledge

Computational modeling is very powerful but be smart

37

MIT OpenCourseWarehttpocwmitedu

3021J 1021J 10333J 18361J 2200J Introduction to Modelling and SimulationSpring 2012

For information about citing these materials or our Terms of Use visit httpocwmiteduterms

Page 30: Part II Lesson 11 A bit more solar PV, some verification & … · 2020. 1. 3. · 1.021, 3.021, 10.333, 22.00 : Introduction to Modeling and Simulation : Spring 2012. Part II –

Wersquove learned a lot

30

Some of the key remaining challenges

The electron correlation problem Only seriously affects a fraction of materials but that fraction tends to contain interesting physics and technological potential At this point there is no logical follow-up on LDAGGA

31

Some of the key remaining challenges

The time-scale problem Short (MD) and long (thermo) is not a problem Intermediate is a problem eg phase transformations

32

Some of the key remaining challenges

The knowledge problem To study an engineering propertybehavior with atomisticshyscale modeling one needs to understand somewhat what controls that property (in order to include the relevant boundary conditions)

eg If a property is controlled by impurities then intrinsic calculations will be irrelevant (Si conductivity)

33

Some of the key remaining challenges

The structure problem Even though the structure-property relation is a key tenet of Materials Science we have only very limited ability to predict structure (crystal structure amorphous microstructure )

34

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons

35

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons Courtesy of Patrick J Lynch License CC-BY

36

Computa(ons13 should13 not13 subs(tute13 for13 lack13 of13

knowledge

Computational modeling is very powerful but be smart

37

MIT OpenCourseWarehttpocwmitedu

3021J 1021J 10333J 18361J 2200J Introduction to Modelling and SimulationSpring 2012

For information about citing these materials or our Terms of Use visit httpocwmiteduterms

Page 31: Part II Lesson 11 A bit more solar PV, some verification & … · 2020. 1. 3. · 1.021, 3.021, 10.333, 22.00 : Introduction to Modeling and Simulation : Spring 2012. Part II –

Some of the key remaining challenges

The electron correlation problem Only seriously affects a fraction of materials but that fraction tends to contain interesting physics and technological potential At this point there is no logical follow-up on LDAGGA

31

Some of the key remaining challenges

The time-scale problem Short (MD) and long (thermo) is not a problem Intermediate is a problem eg phase transformations

32

Some of the key remaining challenges

The knowledge problem To study an engineering propertybehavior with atomisticshyscale modeling one needs to understand somewhat what controls that property (in order to include the relevant boundary conditions)

eg If a property is controlled by impurities then intrinsic calculations will be irrelevant (Si conductivity)

33

Some of the key remaining challenges

The structure problem Even though the structure-property relation is a key tenet of Materials Science we have only very limited ability to predict structure (crystal structure amorphous microstructure )

34

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons

35

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons Courtesy of Patrick J Lynch License CC-BY

36

Computa(ons13 should13 not13 subs(tute13 for13 lack13 of13

knowledge

Computational modeling is very powerful but be smart

37

MIT OpenCourseWarehttpocwmitedu

3021J 1021J 10333J 18361J 2200J Introduction to Modelling and SimulationSpring 2012

For information about citing these materials or our Terms of Use visit httpocwmiteduterms

Page 32: Part II Lesson 11 A bit more solar PV, some verification & … · 2020. 1. 3. · 1.021, 3.021, 10.333, 22.00 : Introduction to Modeling and Simulation : Spring 2012. Part II –

Some of the key remaining challenges

The time-scale problem Short (MD) and long (thermo) is not a problem Intermediate is a problem eg phase transformations

32

Some of the key remaining challenges

The knowledge problem To study an engineering propertybehavior with atomisticshyscale modeling one needs to understand somewhat what controls that property (in order to include the relevant boundary conditions)

eg If a property is controlled by impurities then intrinsic calculations will be irrelevant (Si conductivity)

33

Some of the key remaining challenges

The structure problem Even though the structure-property relation is a key tenet of Materials Science we have only very limited ability to predict structure (crystal structure amorphous microstructure )

34

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons

35

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons Courtesy of Patrick J Lynch License CC-BY

36

Computa(ons13 should13 not13 subs(tute13 for13 lack13 of13

knowledge

Computational modeling is very powerful but be smart

37

MIT OpenCourseWarehttpocwmitedu

3021J 1021J 10333J 18361J 2200J Introduction to Modelling and SimulationSpring 2012

For information about citing these materials or our Terms of Use visit httpocwmiteduterms

Page 33: Part II Lesson 11 A bit more solar PV, some verification & … · 2020. 1. 3. · 1.021, 3.021, 10.333, 22.00 : Introduction to Modeling and Simulation : Spring 2012. Part II –

Some of the key remaining challenges

The knowledge problem To study an engineering propertybehavior with atomisticshyscale modeling one needs to understand somewhat what controls that property (in order to include the relevant boundary conditions)

eg If a property is controlled by impurities then intrinsic calculations will be irrelevant (Si conductivity)

33

Some of the key remaining challenges

The structure problem Even though the structure-property relation is a key tenet of Materials Science we have only very limited ability to predict structure (crystal structure amorphous microstructure )

34

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons

35

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons Courtesy of Patrick J Lynch License CC-BY

36

Computa(ons13 should13 not13 subs(tute13 for13 lack13 of13

knowledge

Computational modeling is very powerful but be smart

37

MIT OpenCourseWarehttpocwmitedu

3021J 1021J 10333J 18361J 2200J Introduction to Modelling and SimulationSpring 2012

For information about citing these materials or our Terms of Use visit httpocwmiteduterms

Page 34: Part II Lesson 11 A bit more solar PV, some verification & … · 2020. 1. 3. · 1.021, 3.021, 10.333, 22.00 : Introduction to Modeling and Simulation : Spring 2012. Part II –

Some of the key remaining challenges

The structure problem Even though the structure-property relation is a key tenet of Materials Science we have only very limited ability to predict structure (crystal structure amorphous microstructure )

34

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons

35

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons Courtesy of Patrick J Lynch License CC-BY

36

Computa(ons13 should13 not13 subs(tute13 for13 lack13 of13

knowledge

Computational modeling is very powerful but be smart

37

MIT OpenCourseWarehttpocwmitedu

3021J 1021J 10333J 18361J 2200J Introduction to Modelling and SimulationSpring 2012

For information about citing these materials or our Terms of Use visit httpocwmiteduterms

Page 35: Part II Lesson 11 A bit more solar PV, some verification & … · 2020. 1. 3. · 1.021, 3.021, 10.333, 22.00 : Introduction to Modeling and Simulation : Spring 2012. Part II –

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons

35

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons Courtesy of Patrick J Lynch License CC-BY

36

Computa(ons13 should13 not13 subs(tute13 for13 lack13 of13

knowledge

Computational modeling is very powerful but be smart

37

MIT OpenCourseWarehttpocwmitedu

3021J 1021J 10333J 18361J 2200J Introduction to Modelling and SimulationSpring 2012

For information about citing these materials or our Terms of Use visit httpocwmiteduterms

Page 36: Part II Lesson 11 A bit more solar PV, some verification & … · 2020. 1. 3. · 1.021, 3.021, 10.333, 22.00 : Introduction to Modeling and Simulation : Spring 2012. Part II –

Theory of Properties The Multi-Scale Materials View

Atoms

Microstructure

Continuum Properties

Electrons Courtesy of Patrick J Lynch License CC-BY

36

Computa(ons13 should13 not13 subs(tute13 for13 lack13 of13

knowledge

Computational modeling is very powerful but be smart

37

MIT OpenCourseWarehttpocwmitedu

3021J 1021J 10333J 18361J 2200J Introduction to Modelling and SimulationSpring 2012

For information about citing these materials or our Terms of Use visit httpocwmiteduterms

Page 37: Part II Lesson 11 A bit more solar PV, some verification & … · 2020. 1. 3. · 1.021, 3.021, 10.333, 22.00 : Introduction to Modeling and Simulation : Spring 2012. Part II –

Computa(ons13 should13 not13 subs(tute13 for13 lack13 of13

knowledge

Computational modeling is very powerful but be smart

37

MIT OpenCourseWarehttpocwmitedu

3021J 1021J 10333J 18361J 2200J Introduction to Modelling and SimulationSpring 2012

For information about citing these materials or our Terms of Use visit httpocwmiteduterms

Page 38: Part II Lesson 11 A bit more solar PV, some verification & … · 2020. 1. 3. · 1.021, 3.021, 10.333, 22.00 : Introduction to Modeling and Simulation : Spring 2012. Part II –

MIT OpenCourseWarehttpocwmitedu

3021J 1021J 10333J 18361J 2200J Introduction to Modelling and SimulationSpring 2012

For information about citing these materials or our Terms of Use visit httpocwmiteduterms