Talk given at IBM Almaden on May 20, 2016

1. 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

2. Making renewable energy a reality
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
new inventions
materials optimization
materials discovery
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 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

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 include:
– simulations & supercomputers
– digital data and data mining
– better merging computation
and experiment
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

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

9. What is density functional theory (DFT)?
9
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i i
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!
+ H = ∇i
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i=1
Ne
∑ + Vnuclear (ri)
i=1
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∑ + Veffective(ri)
i=1
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∑
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.

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

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
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).

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

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

20. A peek into the future?
20

21. Outline
21
① Intro to Density Functional Theory (DFT)
② The Materials Project database
③ Searching for thermoelectric materials
④ Future of Materials Design

22. 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

23. Why are thermoelectrics useful?
23
• 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

24. Thermoelectric figure of merit
24
• 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

25. How zT relates to power generation efficiency
25
C. B. Vining, Nat. Mater. 8, 83 (2009).

26. Thermoelectric materials are improving over time
26
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).

27. We’ve initiated a search for thermoelectric materials
27
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

28. 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

29. 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

30. The limitation - doping
30
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

31. YCuTe2 – friendlier elements, higher zT (0.75)
31
• 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

32. Outline
32
① Intro to Density Functional Theory (DFT)
② The Materials Project database
③ Searching for thermoelectric materials
④ Future of Materials Design

33. DFT methods will become much more powerful
33
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!

34. We will rely more on computers to optimize materials
34
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.

35. http://xkcd.com/1002/

36. 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.

37. 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!
37

38. 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