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

Combining density functional theory calculations, supercomputing, and data-driven methods to understand and design new thermoelectric materials for waste heat recovery

Anubhav Jain (ESDR) ETA Lunchtime Seminar

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

Year1 Year2 Year3 Year4 Year5Li-ion batteries Materials Project JCESR

thermoelectrics

My view of the Energy Technologies Area

2

cost/efforttoimplement+deploynewtechnology

cost/benefittomaintainnewtechnology

cost/benefittoenduseroftoday’stechnology)

STAGE 1 STAGE 2 STAGE 3

carboncapture/storage energyefficiencyretrofitselectricvehiclestoday

SolarCitysolarpanelshybridelectricvehicles

Role of Energy Technologies Area at LBNL

How to move technologies across stages?

3

resourceconstraintsovertimepolicy/carbontax

reducelabor/installationcostpolicy/incentives/rebatesnewbusinessmodels(“leasing”)

bettermanufacturingperformanceengineeringmaterialsoptimizationmaterialsdiscoverynewinventions

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 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?

5

“[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? –  simulations & supercomputers –  digital data and data mining

6 www.whitehouse.gov/mgi

Outline

7

①  Intro to Density Functional Theory (DFT)

②  The Materials Project database

③  Searching for thermoelectric materials

④  Future of Materials Design

⑤  (Brief) thoughts on the Early Career application

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 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?

10

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

How accurate is DFT in practice?

11

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.

12

Time to set sail and leave port!

Outline

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High-throughput DFT: a key idea

14

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

15

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

16

Sidorenkite-based Li-ion battery cathodes

Mn2V2O7 photocatalysts

YCuTe2 thermoelectrics

Yan, Q.; Li, G.; Newhouse, P. F.; Yu, J.; Persson, K. A.; Gregoire, J. M.; Neaton, J. B. Mn2V2O7: An Earth Abundant Light Absorber for Solar Water Splitting, Adv. Energy Mater., 2015

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

17

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

18

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 will be published in: A. Jain, K.A. Persson, G. Ceder. APL Materials (accepted).

Video tutorials are available

19

www.youtube.com/user/MaterialsProject

A peek into the future?

20

A digression about open-source software

•  The Materials Project is the result of many tens of thousands of lines of code –  high-throughput is hard work!

•  We have decided to put it all open-source at

www.github.com/materialsproject

•  Looking back, how has that worked out?

21

v1.2.4

Usage and outreach: •  >7500 downloads per month

•  #1 Google hit for “Python workflow software”

•  #4 software hit for “scientific workflow software”

•  1 of 2 workflow software officially supported by NERSC

•  Several pilot projects at LBNL •  Worldwide usage •  98th percentile, scientific Python

software impact (Depsy)

FireWorks is an application-agnostic workflow software for defining and executing large numbers of calculations

Jain, S.P. Ong, W. Chen, B. Medasani, X. Qu, M. Kocher, M. Brafman, G. Petretto, G.-M. Rignanese, G. Hautier, D. Gunter, and K.A. Persson, Concurr. Comput. Pract. Exp. 22, (2015).

What are some consequences of going open-source?

23

HAPPENED •  I was automatically wrote better code and

documentation•  Tricky but important bugs identified/fixed

by community–  Also new bugs introduced by newcomers (but

quickly fixed)

•  Python 3 compatible by volunteer•  New frontend tools contributed by

volunteer•  Internals became cleaner & user-friendly•  Heated arguments that resulted in

improvements•  Learned about management•  Lots of good feature suggestions, some

feature implementation by community

•  Pace of development greatly accelerated•  Friendly users I had no relation to gradually

came out of the woodwork and asked questions

DID NOT HAPPEN •  Code went viral

–  the world mostly did not notice, especially for the first year

•  Thieves stole the code and didn’t attribute it

–  I think…

•  People blamed me for publishing imperfect code

Outline

24






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

25

www.alphabetenergy.com

Why are thermoelectrics useful?

26

•  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 Uses tetrahedrite (Cu12−xMxSb4S13) materials developed in 2013 by Michigan State/UCLA

Thermoelectric figure of merit

27

•  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

28

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

Thermoelectric materials are improving over time

29

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

30

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

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

Community is developing other models

31

A “quality factor” approach to estimating zT

Yan, J.; Gorai, P.; Ortiz, B.; Miller, S.; Barnett, S. A.; Mason, T.; Stevanović, V.; Toberer, E. S. Material descriptors for predicting thermoelectric performance, Energy Environ. Sci., 2015, 8, 983–994

Thermal conductivity from quasi-harmonic approximation using average of square Gruneisen Madsen, G. K. H.; Katre, A.; Bera, C. Calculating the thermal conductivity of the silicon clathrates using the quasi-harmonic approximation, 1–7.

Thermal conductivity from E-V curves and the GIBBS approximation Toher, C.; Plata, J. J.; Levy, O.; de Jong, M.; Asta, M.; Nardelli, M. B.; Curtarolo, S. High-Throughput Computational Screening of thermal conductivity, Debye temperature and Gruneisen parameter using a quasi-harmonic Debye Model, 2014, 1–15.

Today: 48,000 compounds screened (transport theory modeling to existing Materials Project entries)

32 article submitted, under review

Abundant thermoelectrics: difficulty of oxides •  Oxides would be great: synthesizability, stability, cost •  But they suffer from a triple strike:

–  they are difficult to dope due to wide band gap –  they have higher thermal conductivity –  they have poorer thermoelectric performance independent of these issues

33 Chen, Pöhls, Hautier, Broberg, Bajaj, Aydemir, Gibbs, Zhu, Ceder, Asta, Snyder, Meredig, White, Persson, Jain. Understanding Thermoelectric Properties from High-Throughput Calculations: Trends, Insights, and Comparisons with Experiment. submitted

New Materials from screening – TmAgTe2 (calcs)

34 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

35 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

36

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)

37

•  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

Future: rationally control the band structure

38

example: •  understanding the character of states that form the VBM / CBM •  in TmAgTe2, increased hybridization lowers the valley degeneracy •  Can we predict the orbital character of arbitrary materials?

Jain, A.; Hautier, G.; Ong, S.; Persson, K.A.; New Opportunities for Materials Informatics: Resources and Data Mining Techniques for Uncovering Hidden Relationships. SUBMITTED.

DFT/GGA+U projected DOS for MoO3

Procedure for ranking likelihood to form VBM/CBM •  Data set of 2558 materials

–  ionic materials evaluated via Bond Valence Sum method –  band gap of 0.2 or higher (clear VBM and CBM) –  avoid f-electron materials –  limited pool of elements/orbitals competing for VBM/CBM

•  For each material: –  determine the ionic orbitals (e.g., Mn3+:d, O2-:p, P5+:p) that are present –  determine the contribution of each ionic orbital to VBM/CBM using

projected DOS –  For each pair of ionic orbitals (e.g., Mn3+:d versus O2-:p), score a “win”

for the ionic orbital that contributes more to VBM/CBM

•  Use model to determine universal ranking from the series of pairwise competitions (Bradley-Terry model)

39

Jain, A.; Hautier, G.; Ong, S.; Persson, K.A.; New Opportunities for Materials Informatics: Resources and Data Mining Techniques for Uncovering Hidden Relationships. accepted, J Mat Research

Results: likelihood to form VBM/CBM

40

•  Example interpretation: in a material with Cu1+:d, Fe3+:d, and O2-:p states, the Cu is likely to be VBM and Fe likely to be CBM (this is true for FeCuO2)

•  There are also problems with such a universal ranking (discussed in paper) that require refinement

Jain, A.; Hautier, G.; Ong, S.; Persson, K.A.; New Opportunities for Materials Informatics: Resources and Data Mining Techniques for Uncovering Hidden Relationships. accepted, J Mat Research

Outline

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DFT methods will become much more powerful

42

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!

Materials discovery will incorporate more tools

43

Experimental synthesis & characterization

Broad-based screening

In-depth screening

Experimental optimization

Highly optimized �material

Candidate materials

large chemical library

high-throughput computation

data analysis / machine learning

combinatorial synthesis

detailed simulations

advanced characterization

We will rely more on computers to optimize materials

44

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/

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.

Can we build a general optimizer?

46

Generalizable forward solver

Supercomputing Power

Statistical optimization

FireWorks NERSC MISO/MATSuMoTo

Software for automatically determining next trial based

on collected data (J. Mueller, Computing Sciences)

Materials discovery engine concept

47

Proof of concept: perovskite solar water splitters

48

A B X3 52

metals 52

metals 7 mixtures {O, N, F, S}

(about 19,000 total compounds!)

Optimization algorithms can indeed find new materials! Jain et al., J. Mater. Sci. 48, 6519–6534 (2013).

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

•  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!

49

Outline

50






Tips for the Early Career Application - overall •  Don’t get excluded: make sure your

topic fits within the program call –  most winners seem to have contacted the

program manager in advance to perform this basic check (but don’t break the rules and ask beyond what’s allowed)

•  Think long-term: It’s a five year grant. Be a little optimistic about what can be achieved.

•  Fit into DOE’s goals: It’s not all about you. How does this fit into where DOE is headed?

51

DOE direction

worse proposal, better aligned

better proposal, worse aligned

Tips for the Early Career Application – minor things •  Consider putting the methods section

at the end. –  This allows you to focus on the exciting stuff

more quickly.

•  Maximize your “outs” (Poker strategy). –  Maximize the number of reviewer

combinations that will resonate with your proposal by appealing to a diverse audience and protecting against common criticisms

•  Work on it after it’s “done”. –  Try to finish the proposal 1 week in advance.

This allows you to refine ideas and polish the proposal.

•  If you are a theorist, find a good experimental collaborator and get a letter of support. –  And maybe vice-versa.

52

New this year! ECRP tips are at: http://ecrp.lbl.gov/tips/

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: DOE BES - MSD

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