goddard - msc/caltech pasi-caltech 1/5/04 1 first principles simulations of nanoscale materials...

1GODDARD - MSC/CaltechGODDARD - MSC/Caltech PASI-Caltech 1/5/04PASI-Caltech 1/5/04

First principles Simulations of Nanoscale Materials Technology

William A. Goddard III Charles and Mary Ferkel Professor of

Chemistry, Materials Science, and Applied PhysicsDirector, Materials and Process Simulation Center

California Institute of Technology, Pasadena, California 91125

[http://www.wag.caltech.edu]

NSF Pan-American Advanced Studies Institutes (PASI) Workshop January 5-16, 2004

Computational Nanotechnology and Molecular EngineeringMSC/Caltech: Dr. Mario Blanco and Dr. Mamadou Diallo


Who is Goddard?

Born El Centro, California (in Southern California desert east San Diego) Public schools of El Centro, Delano, Indio, Lodi, Firebaugh, MacFarland, Oildale (Bakersfield), Modesto, Yuma

BS Engineering, UCLA, June, 1960

PhD Engineering Science (Minor physics) Caltech Oct. 1964

Caltech Chemistry Faculty Nov. 1964-2024

108 PhD’s, 550 publications, Member National Academy of Science, Int. Acad. Quantum Molec. Sci.8 patents in protein structure prediction, new polymerization catalysts, semiconducting processing modelingCo-founded 5 companies (all still thriving)

William Goddard PhD (Engr. Sci. 1965,Caltech) advisor: Pol DuwezPol Duwez DSc (1933, Brussels) advisor: Emile Henriot Emile Henriot DSc (Phys, 1912, Sorbonne, Paris) advisor: Marie CurieMarie Curie DSc (1903, Ecole Phys. Chim. Ind, Paris) advisor: Becqeurel


What is Caltech?

Throop University founded in 1891 (at Green and Fair Oaks, near old town Pasadena). vocational college, high school, grammar school

~1905 George Ellery Hale, Astronomer came to Pasadena to build Mt. Wilson Observatory (because of clear air)

In ~ 1907 changed from to Throop Polytechnic Institute (engineering college)

In ~1910 Throop moved to current site (previously a citrus grove)

~1915 Arthur Amos Noyes came from MIT to start science (chemistry) at Caltech

~1918 Noyes attracted Robert A. Millikan to Caltech

In ~ 1920 changed to California Institute of Technology

In 1920’s Nobel Prizes to Mulliken (Physics), Morgan (Biology)


Caltech Today

Six Divisions:

•Chemistry and Chemical Engineering

•Biology

•Geosciences

•Physics, Math, and Astronomy

•Engineering and Applied Science

•Humanities and Social Science

~1000 undergraduates

~1200 graduate students

~350 faculty


What is Beckman Institute?

Gift by Arnold and Mabel Beckman

(Arnold got PhD at Caltech, stayed of faculty for 10 years, then started Beckman Instruments)

Construction finished July,1990

Resource Centers in Chemistry and Biology

•Imaging

•Lasers

•Materials

•Synthesis

•Materials and Process Simulation Center (MSC)


What is Materials and Process Simulation Center? (MSC)

Senior Staff: All involved in nanotechnolgyWilliam A. Goddard III, DirectorBlanco, Director Process Simulations and Industrial TechnologyCagin, Director Mesoscale and Materials Science Technologyvan Duin, Director Force Field and Materials TechnologyVaidehi, Director of Biotechnology and PharmaMeulbroek, Director Software Integration and Databases Oxgaard, Director of Quantum and Catalysis TechnologyDiallo, Manager Molecular Environmental Technology Molinero, Manager of Complex Materials Simulations Willick, Manager of Computer Technology and Networks

Staff: (over 60) Trained in Chem., Phys., Mat. Sci., Appl. Phys., Biol., Envir. Engr., Chem. Eng., Comp. Sci., Elect. Engr.

Excellent team for complex problems

Critical Mass of Expertise in Most areas of Atomistic Theory and Applications to Materials, Chemistry, and Biology

Including QM, FF, MD, MesoDyn, Stat. Mech. Biochem., Catalysis, Ceramics, Polymers, Semicond., Metal Alloys, Nanotechnology, Environmental Technology


Vision of MSC: First Principles Modeling: Base on QMTo predict macroscale from QM use Multiscale Strategy

EOS of different phases, vacancy energy, surface energy

femtosec

time

distance Å nm micron mm meters

MESO

Continuum(FEM)

QM

MD

MD simulations of deformation

Fracture, Plasticity, Creep,Fatigue with application

scales of time-space

ELECTRONS ATOMS GRAINS GRIDS

First principles Force Field

Constitutive relationsmechanisms plastic deformationMesoscale FF (beads)

FF

hours

seconds

microsec

nanosec

picosecBuild fro

m QMFFMD

MesoContinuum


Materials and Process Simulation Center (MSC)

Each student involved with bothdeveloping tools (theory and software) to solve impossible problems (1/2 effort)•using simulation (and experiment) to develop new materials and processes materials (1/2 effort)

Nearly every project has a strong coupling with experimentExperiment stimulates current theory with impossible problemsResults from simulation guide and interpret experimentsExperiment essential to validate theory and simulation

Ultimately, de novo simulation will be at the heart of chemistry, biology, materials science


Theory Essential to solve Grand Challenges in Technology

Materials Science: Nanoscale Technology• Opportunity: Tremendous potential for new functional materials

(artificial machines smaller than cells)• Problems: Synthesis, Characterization, Design• Need Multiscale Modeling: couple time/length scales from

electrons/atoms to manufacturingBiology: Protein Folding: Predict all structures of life• Opportunity Will soon have Genomes for All Life

Now: Over 700,000 genes but only ~10,000 protein structures • Problem: experiment can do only ~ 2000/year ($200 million)• Need: Prediction of Reliable Protein Structure and Function for

1,000,000 proteinsChemistry: Methane (CH4) Activation, Gas to Liquid• Opportunity Enormous reserves of CH4 for energy, chemicals,

and materials, mostly wasted• Problem: no efficient, selective, low temperature catalyst• Need: Predictive Mechanism to predict new catalystsFirst Principles Theory will lead developments of new technologies in 21st Century


BIOTECHNOLGY: Membrane Proteins (GPCR), non-natural Amino Acids, Pharma (VLS)POLYMERS: PEM (nafion), Dendrimers, Gas diffusion, Surface Tension, BiobasedCATALYSTS: Methane Activation, Selective Oxidation, ElectroCat (O2), Polar OlefinsSEMICONDUCTORS: Dielectric Breakdown, Si/SiO2/Si3N4 interfaces, B diffusionCERAMICS: Ferroelectrics, Zeolites, Exfoliation ClaysMETAL ALLOYS: Glass Formation, Plasticity (dislocations, crack propagation, spall) NANOSYSTEMS: DNA based Machines, Carbon Nanotubes, nanoelectronicsENVIRONMENTAL: Dendrimers for Selective Encapsulation, Humic acidINDUSTRIAL APPLICATIONS (GM-GAPC, ChevronTexaco, GM-R&D, Asahi Kasei, Toray)

Polymers: Gas Diffusion, Surface Tension Modification, Water solubilityPolymerization Catalysts for Polar MonomersCatalysts: CH4 activation, Alkylation phenols, zeolites (Acid sites/templates)Semiconductors: Dielectric Breakdown nanometer oxides, nitrides, B Diffusion in Si Automobile Engines: Wear Inhibitors (iron and aluminum based engines)Oil Pipelines: Inhibitors for Corrosion , Scale, Wax; Hydrates, DemulsifiersOil Fields: Surfactants for low water/oil interface energy, Basin modelsCeramics: Bragg Reflection GratingsCatalysts: ammoxidation of propaneFuel Cells: H2 Storage, Polymer Electrolyte Membranes, Electrocatalysis

Applications Focus at the MSC


• GM advanced propulsion: Fuel Cells (store H2, membrane, cathode)• Chevron Corporation: CH4 to CH3OH, Alkylation, Wax Inhibition• General Motors - Wear inhibition in Aluminum engines• Seiko-Epson: Dielectric Breakdown in nm oxide films, TED (B/Si)• Asahi Kasei: Ammoxidation Catalysis, polymer properties• Berlex Biopharma: Structures and Function of CCR1 and CCR5 (GPCRs)• Aventis Pharma: Structures and Function of GPCR’s

Stimulation toward solving impossible problemsCollaborations with Industry

Asahi Glass: Fluorinated Polymers and CeramicsAvery-Dennison: Nanocomposites for computer screens Adhesives, CatalysisBP Amoco: Heterogeneous Catalysis (alkanes to chemicals, EO)Dow Chemical: Microstructure copolymers, Catalysis polymerize polar olefinsExxon Corporation: Catalysis (Reforming to obtain High cetane diesel fuel)Hughes Satellites/Raytheon: Carbon Based MEMSHughes Research Labs: Hg Compounds for HgCdTe from MOMBEKellogg: Carbohydrates/sugars (corn flakes) Structures, water contentMMM: Surface Tension and structure of polymersNippon Steel: CO + H2 to CH3OH over metal catalystsOwens-Corning: Fiberglas (coupling of matrix to fiber)Saudi Aramco: Demulsifiers, AsphaltenesEach project (3 Years) supports full time postdoc and part of a senior scientist


Method Developments in MSC Quantum Mechanics • Solvation (Poisson-Boltzmann)• Periodic Systems (Gaussians)• New Functionals DFT (bond

breaking)• Quantum Monte Carlo methods • Time Dependent DFT (optical spectra)Force Fields• Polarizable, Charge Transfer• Describe Chemical Reactions• Describe Phase Transitions• Mixed Metal, Ceramic, PolymerMesoScale Dynamics• Coarse Grained FF• Kinetic Monte Carlo (Gas Diffusion,

Epitaxial Growth)• Hybrid MD and Meso Dynamics• TribologyUtilization: Integrated, Web-based

Molecular Dynamics• Non-Equilibrium Dynamics

– Viscosity, rheology– Thermal Conductivity

• Solvation Forces (continuum Solv)– surface tension, contact angles

• Hybrid QM/MD• Plasticity

– Formation Twins, Dislocations– Crack Initiation

• Interfacial EnergiesProcess Simulation• Vapor-Liquid Equilibria• Reaction Networks

Method Development critical to progressGenerally not supported by US government or industry


Nanotechnology: Experiment and Theory Closing the Gap

• Opportunity: Nanotechnology provides tremendous potential for new functional materials (artificial machines smaller than cells)

• Problems to be solved before commercialization: – Synthesis – Characterization – Design

• In each area there are tremendous experimental challenges.

• In each case the time to solution will be dramatically decreased by the use of

de novo simulations Multiscale Modeling that couples the time and length scales

from electrons and atoms to manufacturing


Nanoelectronics

Many experimental efforts to make nanoscale electronic devices based on molecules sandwiched between conducting surfaces (e.g., Jim Heath/UCLA-Caltech, Charley Lieber/Harvard, Jan Schön/Lucent, Phaedon Avouris/IBM)

This could be most useful. For example a future MEMS-scale device (say 20 microns in size) might have an onboard computer based on nanometer sized elements with built in sensors and logic to respond to local environment without the necessity of communicating to remote computer.

Thus to be useful the nanosized switches need not be as fast as current computer elements (GHz). They could be even as slow as KHz and still be useful.

Unfortunately little is known about the atomic-level structure and properties of these nanoelectronics systems, making difficult the design of improved devices.


Computational Nanoelectronics

Step 1: Use theory to predict the structures and mechanical properties of Nanoelectronics systems

Step 2: Use theory to predict electrical performance from 1st principles.

To predict electrical performance of experimental systems we must develop QM based methods to predict current/voltage (I/V) performance from first principles.

We have been working on this over the last 2 years (with support from an industrial partner). We can now predict I/V both for isolated molecules and for 2-dimensional slabs (still need to make the software faster for routine use).

We are now in the position to design new systems that can be synthesized and tested.


CBPQT=cyclobis(paraquat-p-phenylene)

DNP=1,5-dioxynaphthalene

[2]Rotaxane

TTF=tetrathiafulvalene;

Stoddart-Heath-Type [2]Rotaxane Molecular Switch

OFFOFF

Weiqiao DengYun Hee JangSeung Soon JangHoon Kim


11_26C KAN242

Volts-1 0 1

Cu

rren

t

0

+2.5 V -2.5 V

Return

11_26e KAN242

Voltage-2 -1 0 1 2

Cu

rren

t

-0

+2V -2.5V

-2.5V +2V Switch diode orientation at –2.3V

NDR=AcceptorNDR=AcceptorCharging(?)Charging(?)

redox

DD

++

++

DD ++

++s

O

Me

ON

Fraser Stoddart (UCLA) Jim Heath Caltech)

OFF

TTF

Naphthyl

TTF

Naphthyl

Molecular Switch

Napthyl and TTF nearly equally good donors Rotaxane ring binds to TTF > 300KRotaxane binds to napthyl > 250K Assume ring moves when apply external voltage which cause diode to switch.

ON

OFF


⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡==−

22221

21

12111

1

HHH

HHH

HHH

HHS

M

MMMM

M

KS

121 ][ −Σ−Σ−−= MMM HEG

)()( 21+ΓΓ= MM GGTraceET

Electrode 2

∫∞

∞−Δ= dEVEpVET

h

eVI ),(),()(

Transmission function:

Δp(E,V) difference in the Fermi-Dirac occupations between electrodes, 1 and 2

Green’s function:

HMM obtained from ab initio QM calculation:Molecule

Spectrum functions:

Green’s functions for electrodes g1 and g2:Describe as Cluster, slab and surface

Ele

ctro

de 1

][ 111+Σ−Σ=Γ i

MM HgH 1111 =Σ][ 222

+Σ−Σ=Γ i

MM HgH 2222 =Σ

Overlap matrix S

Fock matrix H

Ab initio

Self energy:Formalism due to Mark Ratner

Atomic level simulation of Current/VoltageFinite (non periodic) case


Naphthyl

-5.50

-5.40

-5.30

-5.20

-5.10

-5.00

-4.90

-4.80

-4.70

-4.60

-4.50

1.E-05 1.E-04 1.E-03 1.E-02 1.E-01 1.E+00Transmission

energy

TTF

Naphthyl

TTF NaphthylHOMO-1 -6.51 -6.36HOMO -5.56 -5.09LUMO -5.00 -4.93LUMO+1 -4.59 -4.85

TTF

ON

OFF

I-V

-1.5

-1

-0.5

0

0.5

1

1.5

-0.4 -0.2 0 0.2 0.4

Voltage (V)

Current (uA)

TTF

Naphthyl

TTF, OFF

Naphthyl, ON

V(e

nerg

y)

I(transmission)

I(cu

rre

nt)

Voltage

H = HM+Σ

Predicted I(transmission) -V(energy)

Not known experimentally which state is on and which is off


Rotaxane on TTF, OFF Rotaxane on Naphthyl, ON

Nearly degenerate, thus strongly coupled.

~metallic

HOMO LUMOHOMO LUMO

Big gap not coupled.~insulating

Switch mechanism: Location of HOMO and LUMO


Next step on Heath-Stoddart molecular devices

O O O S

S

S

S

OOO

O O O

O O OMe

O OMeO

O O OMe

N

N

N

N

Si Si

Ti / Al Ti / Al

+ +

+

+++

++

++

+++ +

+ +

+ +

+ +

+ +

+ +

+ +

+

+

+ +

+ +

+ +

+ +

+ +

+ +

+ +

+

– e–

+ e–

+

The theory has already shown which site blue box sits on in the On/Off statesWe are now determining how the blue box moves: applied voltage or chemical reductant

Get fundamental understanding of how the device is switched.Currently the switching is done using chemical oxidants, reductants (experiments may take minutes) or by applying a voltage stepIt is not known what the limiting speed is or how it depends on the design

–2e–

+2e–

+

2+

– e– + e– – e–+ e–


Research plan: Optimize molecules for devices

Connection between molecule tail and electrode

NN

OO OO

SSSS

NN NNR

R

SSOO SSSSR

R = NO2, CN, COOH, OH etc

Can do computational experiments in hours

Lab experiments sometimes months

-4

-3

-2

-1

0

1

2

3

4

-0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5

Voltage (V)

Current (uA)

NAP-CN

NAP

TTF-CN

TTF

INAP-CN/INAP = 1.26 Backbone

R = CN:30% increase


Failure of Theory? Bell Labs Molecular Transistor (Schön)

Drain

SiO2

SS SS SSSS

Source

Gate

PURESAM

SAM transistor: Nature (2001) Vol, 413, p. 713Ion/Ioff = 106

Single molecule transistor:Science (2001) Vol 294, p. 2138 Ion/Ioff = 450 at 1:5000 4K temperature

Goals in theoretical Study:• Validate the ability to predict field-effect modulation behavior • Determine the reason for the difference in behavior of these systems•Design new improved systems

SiO2

SS

Source

Gate

Drain

MIXED SAM

SSSS SSSS

Conducting molecules mixed with

insulator molecules

These papers are now in dispute, Results may

have been faked


Shift in Gate voltage can modify MOs near Fermi energy leading to a transistor effect but the maximum effect is only a factor or 20 not the 450 that Schoen reported. Also this value of 20 depends on the optimum placement of the energies of the MOs.

Our work was done in Sept. 2001 and not published since it seemed so inconsistent with experiment.

Since then the experimental results have been withdrawn because of possible fraud.

Thus the theory might be ok.

-6.5

-6

-5.5

-5

-4.5

-4

-3.5

-3

-2.5

-2

0 0.1 0.2 0.3 0.4 0.5

Gate field (V/A)

Energy (eV)

LUMO

HOMO

Under Drain voltage (1V)

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1.80

0 0.05 0.1 0.15 0.2 0.25

Gate Field (V/A)

Drain current (uA)

Molecular orbitals shift under Gate voltage, can shift to fermi energy of electrodes

Predicted field-effect modulation under Gate voltage

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1.80

0.00 0.20 0.40 0.60 0.80 1.00

Drain bias (V)

Drain Current (uA)

0

0.05

0.1

0.15

0.2

0.25

Experiment (Schoen)Field-effect modulation behavior Single molecule transistor

Ion/Ioff = 450

theory

Gate voltage

Drain biasgate field

theory

Ion/Ioff = 20


Z Z Z Z Z

Z Z Z Z Z

Z Z Z Z Z

Z Z Z Z Z

Sir Yes Sir !!

Sir Yes Sir !!

Luo – Collier – Jeppesen – Nielsen – DeIonno – Ho – Perkins – Tseng – Yamamoto – Stoddart – Heath

Best performance Ordered Self Assembled Monolayer

Assumed Could be...

+ + + + + + + +

+ + + + + + + +

+ +

+ +

+ +

+ +

What is the arrangement of Rotaxanes on surface?


Started with half rotaxane 1 (fewer conformations)

• SAM structures at various packing density• Determine How the SAM structure affects

(1) the shuttling motion and(2) the I-V (current-voltage) characteristics

Bryce, et al. Tetrahed. Lett. (2001), 42, 1143Bryce, et al. J. Mater. Chem. (2003) 13, 1

1

SAM of Rotaxane on Au(111)


X-ray structure vs. average from MD

Density 1.59 1.546 (3 % ) 1.801 1.789 (0.67

% ) (g/cm3) @ 295 K @ 295 K @ 160 K @ 160 K

Cooke, Tetrahed. Lett. (2001)

UBULUY

(TTF PQT2+ 2 PF6)

VOLMEO

(TTF CBPQT4+ 4 PF6 4 CH3CN)

R = 0.094; Philp, JCS, Chem. Commun. (1991)

Validate Force field - Crystal structure


1/48(64r3)

3.46 nm2/1

1/36(63r3)

2.59

1/27c(93r3)

1.94

1/24c(64r3)

1.73

1/18c(92r3)

1.30

1/12(32r3)

0.86

UncomplexedComplexedlie downTilt around y-axisUp along yUp along x

SAM at various coverages n(1)/n(Au (111) surf)high low


15

12

48

36

1618

2427

-75

-65

-55

-45

-35

-25

10 15 20 25 30 35 40 45 50

No. of surface Au atoms per 1

BuS only

SAM 1/15 (footage 1.08 nm2/1)

SAM 1/12 (footage 0.86 nm2/1)

Stand up! Ring face (PQT or Ph) parallel to surface: -contact?

Most stable packing coverage of [0.9, 1.3] (nm2/1)


Good -contact between stations

SAM 1/12 (footage 0.86 nm2/2) SAM 1/24 (footage 1.73 nm2/2)

2

Now do full rotaxane 2 (DNP added with ethylene oxide linker)


need to understand the “shoulder-to-shoulder sticking” versus “-stacking” on the surface.

This Could be better...Ideal, assumed

+ + + + + + + +

+ + + + + + + +

+ +

+ +

+ +

+ +

+ +

+ +

+ +

+ +

+ +

+ +

+ +

+ +

z

-Stacking arrangement in [2]rotaxane SAM?


(TTF)(CBPQT)

(PF6)4(DNP)

“CBPQT@TTF”

Only orbitals around EF

(TTF)(DNP)(CBPQT)(PF6)4

“CBPQT@DNP”

Electronic Structure of Model [2]Rotaxane (with shuttle)

HOMO1(DNP)

HOMO(TTF + CBPQT)

LUMO (CBPQT)

LUMO(CBPQT)

HOMO(TTF)

HOMO1(DNP)

• Significant overlap• Shifted to lower

energy• LUMO+1: CBPQT + TTF• LUMO, LUMO+1 Splitting

Ring provides low-lying LUMO’s.Ring stabilizes energy level of nearby station.


extended configurationBetter tunneling between

aligned HOMOs (each at each station:

folded configurationBetter tunneling between

aligned HOMO (free station) and LUMO (ring):

Need direct contact betweenfree station and CBPQT such asfolding and -contact

Two Plausible, Conformation-Dependent, Tunneling Mechanisms in [2]Rotaxane


Exper. UCLA

Theory: Goddard, Caltech


Manipulate Graphene, Graphite, and Single Walled Carbon Nanotubes

Motivation: Hongjai Dai, J. Am. Chem. Soc. 2001, 123, 3838-3839The experiment provided evidence of 1-PBSE absorption by imaging gold clusters presumably attached to the ester tails of the molecules absorbed on SWNT, but what is really happening?

1-Pyrene Butanoic Acid Succinimidyl Ester

Purpose: Understand the interaction of 1-PBSE with carbon nanotubes and with one another so that we can use non-covalent sidewall functionalization of carbon nanotubes to for molecular electronics and self assembly.

concept

The point: people are looking at various devices incorporation SWNTs. Non-covalent functionalization is one way of making compatible with these schemes.


Methods

Step One: Find out how different parts of PBSE interact with one another in free space.

Step Two: Do the same with a PBSE dimer.

Step Four: Examine packed configurations for pyrene and PBSE on graphene and graphite (check this against experimental results if possible).

Step Three: Look at the dynamics of isolated PBSE clusters on graphene and graphite.

Each pyrene covers 50 graphene atoms, each PBSE can cover 24 or 40 atoms.

The point: simulation can complement experiment by revealing things that experiment can't.


More on MethodsStep Six: Put PBSE on (10,10) nanotube and compare the free energies of different packing configurations. Try different nanotube sizes and chiralities.

Validating Tools – Friction: Our tools need to produce the right energy barrier for translation of the absorbed molecules on the graphene surface. We should have the correct stresses for the shearing of graphite.

Validating Tools – Bonding forces: We should have the right shape for our molecules.

Validating Tools – Non-Bond forces: We should produce the correct inter-plane separation in graphite. Our QEq parameters should give the right charges.



Surprises So FarFor isolated PBSE clusters on graphite, both the ester tail and the pyrene part of PBSE like to lay flat.

Van der Waals interaction is responsible for absorption onto graphite, graphene, and nanotube, but there is strong Coulombic interaction between absorbed PBSE’s.

On Graphene, Coulombic interactions between neighboring PBSE’s has led to tighter than expected optimum packing of PBSE. Whereas a single pyrene covered 50 graphene atoms, a single PBSE can cover 24 or 40 graphene atoms. Results on graphite and nanotubes are pending.

Ester tail

Pyrene

PBSE 4x3

PBSE 4x5



SAM packing on Au(111) from MD simulation Coverage-dependence conformation (-stacking)

(1) Electronic structure of essential components from QM Role of the ring 1: provide low-lying LUMO’s Role of the ring 2: stabilize levels of nearby stations -orbitals dominant around HOMO-LUMO

(Aliphatic linkers and anchors negligible?)

(3) I-V Calculation from periodic QM and Green’s matrix ona. Fully extended (unfolded) formb. Fully folded formc. Fully folded form w/o linker/anchor

Conformation effect on I-V

Thru-bond effect on I-V

Summary nanoelectronics


Application: Ferroelectric Actuators Must understand role of domain walls in mediate switching

Switching gives large strain,

… but energy barrier is extremely high!

E

90° domain wall

Domain walls lower the energy barrierby enabling nucleation and growth

Essential questions: Are domain walls mobile? Do they damage the material?In polycrystals? In thin films?

Experiments in BaTiO3

1

2

0 10,000-10,000

0

1.0

Electric field (V/cm)S

trai

n (%

)

Use MD with ReaxFF


)||exp()()(

)||exp()()(

2

2

23

23

si

si

si

si

ci

ci

ci

ci

rrQr

rrQrsi

ci

vvv

vvv

−⋅−=

−⋅−=

ηρ

ηρ

η

η

P-QEq Force Field ModelProper description of Electrostatics is critical

Pair-wise Nonbond Terms between all atomsShort range Pauli Repulsion plus Dispersion (EvdW)

Include Electrostatic interactions between all atoms (ECoulomb) •Describe Charges as distributed (Gaussians)

not point charges •Core charge is fixed to the mass of the atom

(total charge +4 for Ti) •Electron or shell charge is allowed to move

wrt the core (atomic polarizability)•This includes Shielding as charges overlap•Allow charge transfer (shell charges not fixed)•Self-consistent charge equilibration


Charge Equilibration (QEq): Environment dependent charges:

Charge Equilibration (QEq) Require that the chemical potential (dE/dQi) be the same at every atomFix core charge, allow valence charge to transfer and to shift center

Electrostatic energy

Atomic self energy

Pairwise electrostatic interaction(Shielded Coulomb) ( ) l

kkir

rErflj

kiij QQQQrE

ij

ijklij

klij ⎟⎠

⎞⎜⎝

⎛ ⋅+

⋅

=ηη

ηη

,,int

Five universal parameters for each element describes charge distribution, polarization

Original Paper: Rappe and Goddard, J. Phys. Chem. 1991

QEq parameters are fitted to reproduce QM charges on molecules and polarizability of atoms


Use simple Morse Form with 3 universal parameters per ij (R0, D0, ):

Same parameters for same pair of atoms in all environmentsDetermine from QM at very close separations

⎭⎬⎫

⎩⎨⎧

−⋅−−=

=∑≠

)]1(2

exp[2)]1(exp[)(

)(

o

ij

o

ij

r

rMS

r

rMSMSij

MSij

ijji

MSij

MS

DrE

rEE

αα

Distance

ener

gy

Pauli Principle and Dispersion Terms

44


Phase transitions in BaTiO3 from MD

Rhomb. Ortho. Tetra. Cubic

Polarizable ReaxFF


Transition Temperature Transition Temperature (K)(K)

46

Transition Temp.

Rhombohedral to Orthorhombic

Orthorhombic to Tetragonal

Tetragonal to Cubic

Experiment 183 278 393

ReaxFF Between 200-300 Between 300-400 Between 400-500

Phase Rhombohedral

Orthorhombic Tetragonal Cubic

Exp.* 8-9 10-11 15-16 0

ReaxFF 8 10 21 0

* Merz, W. J., Phys. Rev. 76, 1221 (1949)

Spontaneous Polarization Spontaneous Polarization ((uC/cm2)


Hysterisis Loop of BaTiO3 at 300K, 25GHz by MD

47

Apply Dz at f=25GHz (T=40ps).T=300K.

Monitor Pz vs. Dz.

o

PDE

ε

vvv −

=

Get Pz vs. Ez.Ec = 0.05 V/A at f=25 GHz.

Dz

(V/A)

Time (ps)

Applied Field (25 GHz)

Applied Field (V/A)

Polarization (C/cm2)

VDP

VPP

EEEOo

vdwel

εε

vvvv⋅

−⋅

++=3

2

Dipole Correction

Electric Displacement Correction

Ec

Pr


O Vacancy Jump When Applying Strain

48

X-direction strain induces x-site O vacancies (i.e., neighboring Ti’s in x direction) to y or z-sites.

x

z

y

x

z

y

o

O atom

O vacancy site


Effect of O Vacancy on the Hystersis Loop

49

•Introducing O Vacancy reduces both Pr & Ec.

•O Vacancy jumps when domain wall sweeps.

Perfect Crystal without O vacancy

Crystal without 1 O vacancy.O Vacancy jumps when domain wall sweeps.

Supercell: 2x32x2Total Atoms: 640/639

Can look at bipolar case where switch domains from x to y

Ec

Pr


Computational design: Nano-actuator

Nano-assembled structures

•Isolated PVDF chains prefer combination of Trans and Gauch conformations

•Control the packing density by assembling PVDF chains between Si slabs

•when packing density is low structure contains Trans and Gauch conformations

•Can convert to All Trans with an electric field (get large strains, high frequency)

Pt electrodes

Si substrate

PVDF chains


Computational design: nano-actuator

Si (111) surfacePVDF:1/2 coverage

10 monomer long chains

Play movie


Computational design: Nano-actuator

strain

Zero fieldElectric field

Actuation principle

T and G bonds

All trans bonds

Electric field

All trans bonds


Strain-electric field curves

All trans bonds

Electric fieldElectric field

All trans bonds

Zero field

T and G bonds


Strain-polarization field curves

External Electric Field: E(t) = E0 sin(2 t / T)

T = 240 ps ~4 GHzE0 = 0.64 C/m2=~40 MV/m

5 % strain @ 4 GHz

Polarization (C/m2)

stra

in (

%)

ε = Q P2Strain is proportional to square of polarization

Electrostrictive behavior

Obtain from analyze dynamics




T = 120 ps ~8 GHzE0 = 0.64 C/m2=64C/cm2

40 MV/m

Double the frequency




T = 120 ps ~8 GHzE0 = 0.32 C/m2=32C/cm2

Half the electric field

It does not work


Biosynthetic Strategies for Nanoscale Assemblies

Use CURRENT biosynthetic processes to make elements for functional nanoscale devices. NEED:

• fasteners- nanorope, nanorivets (covalent attachments)

• containers- fill and empty upon change in pH, T permanent disposal• Sensors- detect change in local state• Energy source - nano fuel cell (H2/O2) • Templates- to organize parts into a regular array

Use Multiscale Theory to design and characterize systems


Nadrian Seeman (NYU)DNA Double Crossover – JACS 118 6131 (96), Biochem. 32 3211 (93)Nanomechanical Device – Nature 397 144 (99)

•Borromean Ring DNA – Nature 386 137 (97)•3 Arm Junctions – Nucleic Acid Res. 14 9745 (86)•DNA Cube Topology – Nature 350 631 (91)•DNA Nanotechnology – Ann. Rev. Biophy. Biomed.Structures 27 225 (98)

Demonstated DNA Bionanotechnology Synthesis


Nanodevice based on DNA B-Z motorFig. 13 a & 13 b : The nanomechanical device based on B-Z transition device shownin fig. 1. 13a : before operation 13b is after operation.

Double cross over DX in light blue. Each circle represents a DNA double helix.Dark blue circle has a fluorescent quencherB-Z device shown in red.After B-Z transition - quenchingseveral such motors can be mounted in a 2-D array that could cause circles in light blue to move

Before operation

After operation

Design and experimental synthesis and testing: Ned Seeman NYUTheory: Testing and modify: Goddard CaltechFunding: NSF-NIRT (Mike Roco


Current Projects in Polymers

Nanostructure in Nafion membranes for fuel cells

Mesoscale simulations of polysaccharides

Controlled Assembly of Dendrimers into 3D structures (Percec, U. Penn)

Controlled Recognition Sites using Cored Dendrimers (Zimmermann, U. Illinois)

Properties of PAMAM Dendrimers as a function of generation

New Polymers with Low Surface Tension (3M)

Design of polymer membranes for selective Gas Diffusion (Avery Dennison)

Solvent dependent structures of Dendrimer Assemblies (Frechet Berkeley)


Overview of projects on Fuel Cells

2

(~200m)

~10m ~100mPt, 2nm

Polymer electrolyte

membrane fuel cell (PEMFC)

Performance is not adequate but considerable uncertainly regarding: The mechanism of Cathode catalysis, Transport of proton from anode to electrolyte to cathodeRole of polymer nanostructure and interfacesUse atomistic simulations to explain current system and then improve performance


Nafion: Polymer electrolyte Membrane in Fuel Cells

Nafion used in PEMFC (Polymer Electrolyte Membrane Fuel Cell) due to its high proton conductivity as well as electrochemical and mechanical stability.

Molecular level study of nanostructure and transport in hydrated Nafion using atomistic simulation

Most studies on this system have focused on

1. Structure of Nafion 2. Transport characteristics under the various conditions

(water content, temperature etc)

Motivation for studyNeed to run catalyst at 120C to avoid CO poisoning of catalyst, but nafion operates only <80CNeed to develop new generation of non-water membranes that operate at 120C and also do not freeze at -20CNeed first to understand nafion


less blocky(DR=1.1)

not phase-separate

Red: hydrophilicGreen: hydrophobicmore blocky

(DR=0.1)

Get phase separation

hydrophilic domain

How is nanostructure affected by monomeric sequence?

Nanophase-segregation

Objective: investigate the effect of

monomeric sequence on the nanophase-separated structure

and on water transport in hydrated Nafion

Results reported for hydrated Nafion (20 wt % water content)

We find that the monomeric sequence affects

Nanophase-segregated structure Heterogeneity of water/Nafion

interfaceDiffusion of waterElectro-osmotic drag coefficient


ionized D.R.=1.1(20 wt % water)353.15 K

DR (degree of randomness)=1.1DR (degree of randomness)=0.1

ionized D.R.=0.1 (20 wt % water)353.15 K

blocky Nafion has well-developed phase-separated structure: get large hydrophilic channel which percolates and retains bulk-water structure.

80 Å80 Å

Effect of DR on Nanophase-segregation


q: reciprocal vector between i and j (q=2/)

rij: real space vector between i and j

i: local density contrast at location i

L: length of simulation system

Structure factor, S(q)

We find that for low q, DR=0.1 has larger intensity than the DR=1.1.This implies that DR=0.1 (blocky Nafion) has more phase-segregation than DR=1.1.

Use calculated structure to predict the results of Small-angle scattering experiments.SAXS: electron density contrastSANS: deuteron density contrast

How measure the extent of phase-segregation?

Theory can predict many details about how structure and properties and predicts S(q) Experimental measurement of S(q) not give detailed structureCombining theory and experiment to conclude full detail about structure and properties

blocky

dispersed


Atomistic three-phase interface: Catalyst, electrolyte, gas

PolymerPolymerElectrolyteElectrolyte

Pt CatalystPt Catalyst

GasGasPolymerPolymerelectrolyteelectrolyte

How does the proton get from the membrane to the catalyst and delivered to the O to get H2O? We are using ReaxFF to determine atomistic rates.

To obtain low overpotential (activation barrier) may require optimized electrolyte-catalyst interface


Ultrashallow junction fabrication

Junctiondepth (xj)

gate length (L)

gateelectrode

gate oxide

source draine-

MOSFET device

silicon substrateGreatly decreased gate length (100 nm) requires ultrashallow junctions (30nm)to minimize short channel effects.

xj (nm) 30-80 20-70 15-45 10-30

Year 2001 2004 2007 2010

L (m) (0.18) (0.13) (0.10) (0.07)


What are the issues?

Requires Better understanding ofRequires Better understanding of • low-energy implantation• transient enhanced dopant diffusion (TED)• defect/dopant clustering

Requires Development of predictive numerical models

Process requirementsProcess requirements• maximize dopant activation• minimize dopant diffusion• attain box-like doping profiles

Annealing (~ 1000 oC)

B+ implantation (< 1 keV)

Si substrate

z

CB

B

B

z

TED

z

CB


Multiscale modeling and simulation

fundamental data

Kinetic Monte Carlo simulations [long time (>1 sec)]

Atomic-scale calculations• density functional theory• tight binding MD• classical MD

explanation/prediction

validation

Experimental data

100

B+

200 300 400 500 600 700Depth, Å

B c

onc

ent

ratio

n, c

m-3

1021

1022

1020

1019

1018

as implanted

after annealing (1-10 sec)

Technological Problem: Transient Enhanced DiffusionLong range tail. Made worse by pronounced shoulderExplanation: Bs-SiI migration + BsB migration


Orange balls = Si atoms green balls = B atoms

[B-B]s [B-B]s

Bs -Bi Bs -Bi

Bs-Bs-I Bs-Bs-I

TS

(a)

(b)

(c)(e)

(d) (f)

(g)

diffusion pathway for a boron-boron pair.


Kinetic Monte Carlo simulation (CB=Csi=1019 cm-3, T=900 K)

Boron

Si interstitial

Si cluster

I2

t=0 secI3 I5

I2

I10

t=10-6 sec

t=10-3 sec

t=1 sec


1018

1019

1020

1021

0 300200100 400 500 600

Depth, A

Con

cent

ratio

n, c

m-3

1 keV B+ implantation

1000 oC

950 oC

kink formation

concave & shouldering

diffusion profile evolution experimental vs. simulation


Computational Materials Design Facility (CMDF)Funded by DARPA

To integrate these disparate software into a Virtual Test FacilityWe are developing the CMDF (an integrated Materials Design Facility)• ICARUS visualization and GUI environment• Common-Generalized Materials Data Model• External-Plug in Materials Properties Simulation Tools as modules• Driven by Python as scripting Language• Archiving MP simulations in MP Database• Query/Search MP database• Generate and regenerate Materials Properties• Sockets for 3rd party MP application modules

Materials Properties Modeling software spans modeling scales from electrons, to atoms, to mesoatoms(QM, FF, MM/MD/MC, unit processes, analysis)


First Principles Reactive force fields: strategy

• Describe Chemistry (i.e., reactions) of molecules-Fit QM Bond dissociation curves for breaking every type of bond

(XnA-BYm), (XnA=BYm), (XnA≡BYm)-Fit angle bending and torsional potentials from QM-Fit QM Surfaces for Chemical reactions (uni- and bi-molecular)-Fit Ab initio charges and polarizabilities of molecules

•Pauli Principle: Fit to QM for all coordinations (2,4,6,8,12)•Metals: fcc, hcp, bcc, a15, sc, diamond•Defects (vacancies, dislocations, surfaces)•cover high pressure (to 50% compression or 500GPa)

•Generic: use same parameters for all systems (same O in O3, SiO2, H2CO, HbO2, BaTiO3)

Require that One FF reproduces all the ab-initio data (ReaxFF)


ReaxFF Electrostatics energy

Three universal parameters for each element:1991 paper : use experimental IP, EA, Ri; ReaxFF get from fitting QM

ci

si

ci

oi

oi qRRJ &,,,?

Keeping: ∑ =i

i Qq

•Self-consistent Charge Equilibration (QEq)•Describe charges as distributed (Gaussian)•Thus charges on adjacent atoms shielded (interactions constant as R0) and include interactions over ALL atoms, even if bonded (no exclusions)•Allow charge transfer (QEq method)

( ) ∑ ∑<

⎟⎠

⎞⎜⎝

⎛ ++=ji i

iiiiijjiiji qJqrqqJqE2

1),,(}{ χ

Electronegativity (IP+EA)/2

Hardness (IP-EA)

interactions atomic

( ) lk

kir

rErflj

kiij QQQQrE

ij

ijklij

klij ⎟⎠

⎞⎜⎝

⎛ ⋅+

⋅

=ηη

ηη

,,int

Jij

rij

1/rij

ri0

+ rj0

I

2


Reactive Force Field - ReaxFF

VdWCoulVal EEEE ++=Valence energy

Electrostatic energy

short distance Pauli Repulsion + long range dispersion(pairwise Morse function)

•Valence Terms (EVal) based on Bond Order: dissociates smoothly• Bond distance Bond order Bond energy•Allow angle, torsion, and inversion terms where needed•Includes resonance (benzene, allyl)•describes forbidden (2s + 2s) and allowed (Diels-Alder) reactions •Atomic Valence Term (sum of Bond Orders gives valency)

•Pair-wise Nonbond Terms between all atoms•Short range Pauli Repulsion plus Dispersion (EvdW)


Applications of ReaxFF

• Decomposition of High Energy (HE) Density Materials (HEDM)• MD simulations of shock decomposition and of cook-off• Reaction Kinetics from MD simulations• Ferroelectric oxides (BaTiO3) domain structure, • Pz/Ez Hysteresis Loop of BaTiO3 at 300K, 25GHz by MD• Catalysts: Pt Fuel cell anode, cathode, CH transformations• Ni catalyzed growth of bucky tubes• MoOx catalysts: catalyzed growth of bucky tubes• Si-SiO2 and Si-SiOxNy interfaces• Metal alloy phase transformations (crystal-amorphous)• Enzyme Proteolysis • Si-Al-Mg oxides: Zeolites, clays, mica, intercolation with

polymers• MoVNbTaTeOx (Mitsubishi) ammoxidation catalysts

GODDARD - MSC/CaltechGODDARD - MSC/Caltech PASI-Caltech 1/5/04PASI-Caltech 1/5/04

QM/ReaxFF interaction: new RDX decomposition pathway discovered by

ReaxFF; validated by QM

-80

-60

-40

-20

0

20

40

60

80

100

Energy (kcal/mol) RDX

RDX'+HONO

RDX''+2HONO

TAZ+3HONO3N2O+3H2CO

LM2+2N2O+

2H2CO MN+2N2O+

2H2CO

MN+LM2+

N2O+H2CO2MN+N2O+

H2CO

3MN2MN+LM2

RDR+

NO2

RDRo+

NO2INT176+

NO2

INT149+

HCN+NO2

MN+MNH+

HCN+NO2

NNONNNONOOOOHONNNNOOOOHHONNNOOONNNO2ONN

O

N

NC

N

O

O

H

O

N

NC

N

O

O

H

+N2O

+N2O+HONO

+N2O+HONO

+N2O+HONO

+N2O+2 HONO

H2C=O+N2O+2 HCN+2 HONO

N N

N

O2N N

NO2

O

ON N

N

O2N N

NO2

O

O

HONO elimination

NO2 dissociation

concertedNew mechanism

QMReaxFF

8 membered ring

New unimolecular reactionInspired by ReaxFF MD simulationMore important than concerted pathway

Concerted, NO2 and HONO-dissociation pathways(part of the original training set)


QM + ReaxFF opens door to Complex Reactions

• Use QM to Extend and develop the detailed reaction mechanism for reactions of organics, organometallics– Unimolecular and bimolecular reactions

• Extend ReaxFF to fit all possible unimolecular and bimolecular reactions.

• Use ReaxFF in computational experiments to describe fundamental processes as function temperature and pressure including uni-,bi-,ter, -tetra-molecular reactions

• Include defects, finite grain size, binder, plasticizer in these simulations to determine models for their effect on microstructure and performance of materials

ReaxFF references:1: van Duin, A.C.T.; Dasgupta, S.; Lorant, F.; Goddard III, W.A. J. Phys. Chem. A 2001, 105, 9396.2: van Duin, A.C.T.; Strachan, A.; Stewman,S; Zhang, Q.; Goddard III, W.A., J. Phys. Chem. A 2003, 107, 3803.3: Strachan, A.; van Duin, A.C.T.; Chakraborty, D.; Dasgupta, S.;Goddard III, W.A. Phys. Rev. Letters. 2003, 91, 098301. 4. Zhang. Q.; Cagin, T.; van Duin, A.C.T.; Goddard III, W.A. Phys. Rev. B, subm


Conclusions

Goddard

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