Combining density functional theory calculations, supercomputing, and data-driven methods, the speaker aims to understand and design new thermoelectric materials for waste heat recovery. He discusses using high-throughput computations and large databases like the Materials Project to efficiently search for promising thermoelectric materials candidates among thousands of potential compositions. Experimental validation is then needed to confirm computational predictions.

1. 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
Year 1 Year 2 Year 3 Year 4 Year 5
Li-ion batteries
Materials Project
JCESR
thermoelectrics

2. My view of the Energy Technologies Area
2
cost/effort to
implement+deploy
new technology
cost/benefit
to maintain new
technology
cost/benefit
to end user
of today’s
technology)
STAGE 1 STAGE 2 STAGE 3
carbon capture/storage energy efficiency retrofits
electric vehicles today
SolarCity solar panels
hybrid electric vehicles
Role of Energy Technologies Area at LBNL

3. How to move technologies across stages?
3
resource constraints over time
policy / carbon tax
reduce labor/installation cost
policy / incentives / rebates
new business models (“leasing”)
better manufacturing
performance engineering
materials optimization
materials discovery
new inventions
areas that
I work on
ETA has a broad portfolio that encompasses a mix of strategies

4. 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

5. 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)

6. 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

7. 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

8. An overview of materials modeling techniques
8
Source: NASA

9. What is density functional theory (DFT)?
9
+ )};({
)};({
trH
dt
trd
i i
i
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.

10. How does one use DFT to design new materials?
10
A. Jain, Y. Shin, and K. A.
Persson, Nat. Rev. Mater.
1, 15004 (2016).

11. 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).

12. 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!

13. Outline
13
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

14. 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)

15. 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.

16. 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).

17. 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!

18. 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).

19. Video tutorials are available
19
www.youtube.com/user/MaterialsProject

20. A peek into the future?
20

21. 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

22. 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).

23. 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

24. Outline
24
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

25. 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

26. 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

27. 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 scatterin

28. How zT relates to power generation efficiency
28
C. B. Vining, Nat. Mater. 8, 83 (2009).

29. 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).

30. 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

31. 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.

32. Today: 48,000 compounds screened
(transport theory modeling to existing Materials Project entries)
32
article submitted, under review

33. 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

34. 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

35. 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

36. The limitation - doping
36
p=1020
VB Edge CB Edge
n=1020
1016
E-Ef (eV)
TmAgTe2
600K
Our
Sample
2 1
3
4
1
2
4
3
Te Te
Tm AgY AgTmAg 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

37. 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

38. 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

39. 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

40. 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

41. Outline
41
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

42. 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!

43. 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

44. 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.

45. http://xkcd.com/1002/
NASA antenna design
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.

46. 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)

47. Materials discovery engine concept
47

48. 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).

49. 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

50. Outline
50
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

51. 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

52. 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/

53. 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