combining density functional theory calculations, supercomputing, and data-driven methods to design...

Combining density functional theory calculations, supercomputing, and data-driven methods to design new thermoelectric materials

Anubhav Jain Energy Technologies Area

Lawrence Berkeley National Laboratory Berkeley, CA

Slides posted to http://www.slideshare.net/anubhavster

Making renewable energy a reality

2

cost/effort to implement+deploynew technology

cost/benefitto maintain new technology

cost/benefit to end userof today’s technology)

STAGE 1 STAGE 2 STAGE 3

carboncapture/storage energyefficiencyretrofitselectricvehiclestoday

SolarCitysolarpanelshybridelectricvehicles

Role of Energy Technologies Area at LBNL

How to move technologies across stages?

3

resource constraints over timepolicy / carbon tax

reduce labor/installation costpolicy / incentives / rebatesnew business models (“leasing”)

better manufacturingperformance engineeringnew inventionsmaterials optimizationmaterials discovery

areas that I work on

ETA has a broad portfolio that encompasses a mix of strategies

Better materials are an important but difficult route

•  Novel materials with enhanced performance characteristics could make a big dent in sustainability, scalability, and cost

•  In practice, we tend to re-use the same fundamental materials for decades –  solar power w/Si since 1950s –  graphite/LCO (basis of today’s Li battery electrodes)

since 1990

•  Why is discovering better materials such a challenge?

4

How does traditional materials discovery work?

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“[The Chevrel] discovery resulted from a lot of unsuccessful experiments of Mg ions insertion into well-known hosts for Li+ ions insertion, as well as from the thorough literature analysis concerning the possibility of divalent ions intercalation into inorganic materials.”

-Aurbach group, on discovery of Chevrel cathode

Levi, Levi, Chasid, Aurbach J. Electroceramics (2009)

Can we invent other, faster ways of finding materials?

•  The Materials Genome Initiative thinks it is possible to “discover, develop, manufacture, and deploy advanced materials at least twice as fast as possible today, at a fraction of the cost”

•  Major components of the strategy include: –  simulations & supercomputers –  digital data and data mining –  better merging computation

and experiment 6 www.whitehouse.gov/mgi

Outline

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①  Intro to Density Functional Theory (DFT)

②  The Materials Project database

③  Searching for thermoelectric materials

④  Future of Materials Design

An overview of materials modeling techniques

8 Source: NASA

What is density functional theory (DFT)?

9

+ )};({)};({ trHdt

trdi ii Ψ=

Ψ ∧

!+H = ∇i

2

i=1

Ne

∑ + Vnuclear (ri)i=1

Ne

∑ + Veffective(ri)i=1

Ne

∑

DFT is a method to solve for the electronic structure and energetics of arbitrary materials starting from first-principles. In theory, it is exact for the ground state. In practice, accuracy depends on many factors, including parameters, the type of material, the property to be studied, and whether the simulated crystal is a good approximation of reality. DFT resulted in the 1999 Nobel Prize for chemistry (W. Kohn). It is responsible for 2 of the top 10 cited papers of all time, across all sciences.

How does one use DFT to design new materials?

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A. Jain, Y. Shin, and K. A. Persson, Nat. Rev. Mater. 1, 15004 (2016).

How accurate is DFT in practice?

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Shown are typical DFT results for (i) Li battery voltages, (ii) electronic band gaps, and (iii) bulk modulus

(i) (ii)

(iii)

(i) V. L. Chevrier, S. P. Ong, R. Armiento, M. K. Y. Chan, and G. Ceder, Phys. Rev. B 82, 075122 (2010). (ii) M. Chan and G. Ceder, Phys. Rev. Lett. 105, 196403 (2010). (iii) M. De Jong, W. Chen, T. Angsten, A. Jain, R. Notestine, A. Gamst, M. Sluiter, C. K. Ande, S. Van Der Zwaag, J. J. Plata, C. Toher, S. Curtarolo, G. Ceder, K.A. Persson, and M. Asta, Sci. Data 2, 150009 (2015).

Viewpoint of the DFT accuracy situation

•  More accurate would certainly be better –  Many researchers are

working on this problem, including MSD at LBNL and UC Berkeley

–  New and better methods do appear over time, e.g., hybrid functionals for solids.

•  But – let’s not wait for perfection before we start applying it.

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Time to set sail and leave port!

Outline

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High-throughput DFT: a key idea

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Automate the DFT procedure

Supercomputing Power

FireWorks

Software for programming general computational workflows that can be scaled across large

supercomputers.

NERSC

Supercomputing center, processor count is ~100,000 desktop

machines. Other centers are also viable.

High-throughput materials screening

G. Ceder & K.A. Persson, Scientific American (2015)

Examples of (early) high-throughput studies

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Application Researcher Search space Candidates Hit rate

Scintillators Klintenberg et al. 22,000 136 1/160

Curtarolo et al. 11,893 ? ?

Topological insulators Klintenberg et al. 60,000 17 1/3500

Curtarolo et al. 15,000 28 1/535

High TC superconductors Klintenberg et al. 60,000 139 1/430

Thermoelectrics – ICSD - Half Heusler systems - Half Heusler best ZT

Curtarolo et al. 2,500 80,000 80,000

20 75 18

1/125 1/1055 1/4400

1-photon water splitting Jacobsen et al. 19,000 20 1/950

2-photon water splitting Jacobsen et al. 19,000 12 1/1585

Transparent shields Jacobsen et al. 19,000 8 1/2375

Hg adsorbers Bligaard et al. 5,581 14 1/400

HER catalysts Greeley et al. 756 1 1/756*

Li ion battery cathodes Ceder et al. 20,000 4 1/5000*

Entries marked with * have experimentally verified the candidates. See also: Curtarolo et al., Nature Materials 12 (2013) 191–201.

Computations predict, experiments confirm

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Sidorenkite-based Li-ion battery cathodes

Carbon capture

YCuTe2 thermoelectrics

Dunstan, M. T., Jain, A., Liu, W., Ong, S. P., Liu, T., Lee, J., Persson, K. A., Scott, S. A., Dennis, J. S. & Grey, C. Large scale computational screening and experimental discovery of novel materials for high temperature CO2 capture. Energy and Environmental Science (2016)

Chen, H.; Hao, Q.; Zivkovic, O.; Hautier, G.; Du, L.-S.; Tang, Y.; Hu, Y.-Y.; Ma, X.; Grey, C. P.; Ceder, G. Sidorenkite (Na3MnPO4CO3): A New Intercalation Cathode Material for Na-Ion Batteries, Chem. Mater., 2013

Aydemir, U; Pohls, J-H; Zhu, H; Hautier, G; Bajaj, S; Gibbs, ZM; Chen, W; Li, G; Broberg, D; White, MA; Asta, M; Persson, K; Ceder, G; Jain, A; Snyder, GJ. Thermoelectric Properties of Intrinsically Doped YCuTe2 with CuTe4-based Layered Structure. J. Mat. Chem C, 2016

More examples here: A. Jain, Y. Shin, and K. A. Persson, Nat. Rev. Mater. 1, 15004 (2016).

Another key idea: putting all the data online

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Jain*, Ong*, Hautier, Chen, Richards, Dacek, Cholia, Gunter, Skinner, Ceder, and Persson, APL Mater., 2013, 1, 011002. *equal contributions

The Materials Project (http://www.materialsproject.org)

free and open >17,000 registered users around the world >65,000 compounds calculated

Data includes •  thermodynamic props. •  electronic band structure •  aqueous stability (E-pH) •  elasticity tensors

>75 million CPU-hours invested = massive scale!

The data is re-used by the community

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K. He, Y. Zhou, P. Gao, L. Wang, N. Pereira, G.G. Amatucci, et al., Sodiation via Heterogeneous Disproportionation in FeF2 Electrodes for Sodium-Ion Batteries., ACS Nano. 8 (2014) 7251–9.

M.M. Doeff, J. Cabana, M. Shirpour, Titanate Anodes for Sodium Ion Batteries, J. Inorg. Organomet. Polym. Mater. 24 (2013) 5–14.

Further examples in: A. Jain, K.A. Persson, G. Ceder. APL Materials (2016).

Video tutorials are available

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www.youtube.com/user/MaterialsProject

A peek into the future?

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Outline

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Thermoelectric materials •  A thermoelectric material

generates a voltage based on applied thermal gradient –  picture a charged gas that

diffuses from hot to cold until the electric field balances the thermal gradient

•  The voltage per Kelvin is the Seebeck coefficient

•  A thermoelectric module improves voltage and power by linking together n and p type materials

22

www.alphabetenergy.com

Why are thermoelectrics useful?

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•  Applications: energy from heat, refrigeration •  Already used in spacecraft and high-end car

seat coolers •  Large-scale waste heat recovery is targeted

Alphabet Energy – 25kW generator

Thermoelectric figure of merit

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•  Require new, abundant materials that possess a high “figure of merit”, or zT, for high efficiency

•  Target: zT at least 1, ideally >2

ZT = α2σT/κ

power factor >2 mW/mK2

(PbTe=10 mW/mK2)

Seebeck coefficient > 100 �V/K Band structure + Boltztrap

electrical conductivity > 103 /(ohm-cm) Band structure + Boltztrap

thermal conductivity < 1 W/(m*K) •  �e from Boltztrap •  �l difficult (phonon-phonon scattering)

How zT relates to power generation efficiency

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C. B. Vining, Nat. Mater. 8, 83 (2009).

Thermoelectric materials are improving over time

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Also, many new materials have been recently discovered around the zT=1 range, e.g. tetrahedrites

SnSe zT=2.62 reported in 2014

J. P. Heremans, M. S. Dresselhaus, L. E. Bell, and D. T. Morelli, Nat. Nanotechnol. 8, 471 (2013).

G. J. Snyder and E. S. Toberer, 7, 105 (2008).

We’ve initiated a search for thermoelectric materials

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Initial procedure similar to Madsen (2006) On top of this traditional procedure we add: •  thermal conductivity

model of Pohl-Cahill •  targeted defect

calculations to assess doping

•  Today - ~50,000 compounds screened!

Madsen, G. K. H. Automated search for new thermoelectric materials: the case of LiZnSb. J. Am. Chem. Soc., 2006, 128, 12140–6

New Materials from screening – TmAgTe2 (calcs)

28 Zhu, H.; Hautier, G.; Aydemir, U.; Gibbs, Z. M.; Li, G.; Bajaj, S.; Pöhls, J.-H.; Broberg, D.; Chen, W.; Jain, A.; White, M. A.; Asta, M.; Snyder, G. J.; Persson, K.; Ceder, G. Computational and experimental investigation of TmAgTe 2 and XYZ 2 compounds, a new group of thermoelectric materials identified by first-principles high-throughput screening, J. Mater. Chem. C, 2015, 3

TmAgTe2 - experiments

29 Zhu, H.; Hautier, G.; Aydemir, U.; Gibbs, Z. M.; Li, G.; Bajaj, S.; Pöhls, J.-H.; Broberg, D.; Chen, W.; Jain, A.; White, M. A.; Asta, M.; Snyder, G. J.; Persson, K.; Ceder, G. Computational and experimental investigation of TmAgTe 2 and XYZ 2 compounds, a new group of thermoelectric materials identified by first-principles high-throughput screening, J. Mater. Chem. C, 2015, 3

The limitation - doping

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p=1020

VB Edge CB Edge

n=1020

1016

E-Ef (eV)

TmAgTe2600K

Our Sample

2 1

3 4

1 2

4 3

Te Te

Tm Ag Y Ag TmAg TmAg2 YAg

TmTe TmAgTe2

Ag2Te

YTe YAgTe2

Ag2Te

Y6AgTe2

Region 1 Region 2

Region 3 Region 4

•  Calculations indicate TmAg defects are most likely “hole killers”.

•  Tm deficient samples so far not successful •  Meanwhile, explore other chemistries

YCuTe2 – friendlier elements, higher zT (0.75)

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•  A combination of intuition and calculations suggest to try YCuTe2

•  Higher carrier concentration of ~1019

•  Retains very low thermal conductivity, peak zT ~0.75

•  But – unlikely to improve further

Aydemir, U.; Pöhls, J.-H.; Zhu, H.l Hautier, G.; Bajaj, S.; Gibbs, Z. M.; Chen, W.; Li, G.; Broberg, D.; Kang, S.D.; White, M. A.; Asta, M.; Ceder, G.; Persson, K.; Jain, A.; Snyder, G. J. YCuTe2: A Member of a New Class of Thermoelectric Materials with CuTe4-Based Layered Structure. J. Mat Chem C, 2016

experiment

computation

Outline

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DFT methods will become much more powerful

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types of materials

high-throughput screening

computations predict materials?

relative computing power

1980s simple metals/semiconductors

unimaginable by almost anyone

unimaginable by majority

1

1990s + oxides unimaginable by majority

1-2 examples 1000

2000s + complex/correlated systems

1-2 examples ~5-10 examples 1,000,000

2010s +hybrid systems +excited state properties?

~many dozens of examples

~25 examples, maybe 50 by end of decade

1,000,000,000*

2020s ?linear scaling? ?routine? ?routine? ?1 trillion?

* The top 2 DOE supercomputers alone have a budget of 8 billion CPU-hours/year, in theory enough to run basic DFT characterization (structure/charge/band structure) of ~40 million materials/year!

We will rely more on computers to optimize materials

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During World War II, no team of human cryptographers could decode the German Enigma machine. Alan Turing succeeded where others failed for two reasons: 1.  He built a very large computing

machine that could test whether a given parameter combination represented a good solution

2.  When brute force was not enough, he devised clever statistical tests to greatly narrow down the possibilities to assist the computer

A similar system might be useful for materials optimization.

http://xkcd.com/1002/

http://xkcd.com/1002/

NASAantennadesign

http://en.wikipedia.org/wiki/Evolved_antenna

this antenna is the product of a radiation model+genetic algorithm solver. It was better than human designs and launched into space.

But remember…

•  Accuracy will always be an issue •  Not everything can be simulated

–  today, you are lucky if you can simulate 20% of what you want to know about a material for an application with decent accuracy

•  Even with many improvements to current technology,

this will still just be a tool in materials discovery and never a complete solution

•  But – perhaps we can indeed cut down on materials discovery time by a factor of two!

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Thank you!

•  Dr. Kristin Persson and Prof. Gerbrand Ceder, founders of Materials Project and their teams

•  Prof. Shyue Ping Ong (pymatgen) •  Prof. Geoffroy Hautier (thermoelectrics) •  Prof. Jeffrey Snyder + team (thermoelectrics) •  Prof. Mary Anne White + team (thermoelectrics) •  Prof. Mark Asta and team (thermoelectrics) •  Prof. Karsten Jacobsen + team (perovskite GA) •  NERSC computing center and staff

•  Funding: U.S. Department of Energy

38 Slides posted to http://www.slideshare.net/anubhavster