Talk given at MRS Fall 2016 in Boston, MA on Nov 30

1. Combining High-Throughput Computing and
Statistical Learning to Develop and Understand New
Thermoelectric Compounds
Anubhav Jain
Energy Technologies Area
Lawrence Berkeley National Laboratory
Berkeley, CA
MRS Fall 2016
Slides (already) posted to http://www.slideshare.net/anubhavster

2. Thermoelectric materials convert heat to electricity
• A thermoelectric material
generates a voltage
based on thermal
gradient
• Applications
– Heat to electricity
– Refrigeration
• Advantages include:
– Reliability
– Easy to scale to different
sizes (including compact)
2
www.alphabetenergy.com
Alphabet Energy – 25kW generator

3. Thermoelectric figure of merit
3
• 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)
• Very difficult to balance these properties
using intuition alone!

4. Example: Seebeck and conductivity tradeoff
4
Heavy band:
ü Large DOS
(higher Seebeck and more carriers)
✗Large effective mass
(poor mobility)

5. Example: Seebeck and conductivity tradeoff
5
Heavy band:
ü Large DOS
(higher Seebeck and more carriers)
✗Large effective mass
(poor mobility)
Light band:
ü Small effective mass
(improved mobility)
✗Small DOS
(lower Seebeck, fewer carriers)

6. Example: Seebeck and conductivity tradeoff
6
Heavy band:
ü Large DOS
(higher Seebeck and more carriers)
✗Large effective mass
(poor mobility)
Light band:
ü Small effective mass
(improved mobility)
✗Small DOS
(lower Seebeck, fewer carriers)
Multiple bands, off symmetry:
ü Large DOS with small
effective mass
✗Difficult to design!

7. We’ve initiated a search for new bulk thermoelectrics
7
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
Chen, W. et al. Understanding thermoelectric properties from high-
throughput calculations: trends, insights, and comparisons with
experiment. J. Mater. Chem. C 4, 4414–4426 (2016).

8. Going beyond constant relaxation time - AMSET
• Fully ab initio mobility and Seebeck
including realistic scattering effects
• Previously aMOBT (Washington
University in St. Louis)
• Parameterizes the band structure
into 1D
– Misses anisotropic effects and doesn’t
fully treat multi-band effects (for now)
• Uses scattering expressions derived
by previous work by Rode with DFT
parameters
– ionized impurity scattering
– deformation potential scattering
– piezoelectric scattering
– polar optical phonon
8
Faghaninia, A., Ager, J. W. & Lo, C. S. Ab initio electronic transport model with explicit solution to the linearized Boltzmann
transport equation. Phys. Rev. B 91, 235123 (2015).

9. Transport database
9
All data will be made
available via upcoming
publication as well as on
Materials Project
• Seebeck
• conductivity/tau
• effective mass
• electronic thermal conductivity

10. New Materials from screening – TmAgTe2 (calcs)
10
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

11. TmAgTe2 (experiments)
11
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

12. YCuTe2 – friendlier elements, higher zT (0.75)
12
• 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

13. Bournonites – CuPbSbS3 and analogues
• Natural mineral
• Measured thermal conductivity for
CuPbSbS3 < 1 W/m*K
– Stereochemical lone pair scattering
mechanisms
• Measured Seebeck coefficient in
the range of 400 µV/K
• BUT electrical conductivity likely
requires improvement – can
calculations help?
• Total of 320 substitutions into
ABCD3 formula computed
• Experimental study is next
13
Faghaninia A., Yu G., Aydemir U., Wood M., Chen W., Rignanese G.M., Snyder J., Hautier G., Jain, A. A computational assessment
of the electronic, thermoelectric, and defect properties of bournonite (CuPbSbS3) and related substitutions (submitted)

14. Variation of properties with substitution
14

15. Variation of properties with substitution
15
B and C groups (lone pair sites) require heavier elements for
stability (low Eh) – Si and N are very unstable!

16. Variation of properties with substitution
16
As expected, band gaps tend to decrease with heavier anions
This is due to shifting up of the VBM level

17. Variation of properties with substitution
17

18. Variation of properties with substitution
18
Cu has lowest bandgap because Cu1+ also tends to be very high up
in the valence band

19. Variation of properties with substitution
19
Jain, A., Hautier, G., Ong, S. P. & Persson, K. New opportunities for
materials informatics: Resources and data mining techniques for
uncovering hidden relationships. J. Mater. Res. 31, 977–994 (2016).

20. Interesting bournonites and effect of scattering
20
AMSET indicates interband scattering is extremely significant – need to confirm
Substitutions listed
here are close to
thermodynamic
stability
(<0.05 eV /atom
unstable)

21. Defects – selenide looks slightly better than sulfide
21
(a) (b)
• Multiple defects prevent n-type formation
• p-type limited by SbPb defect. Situation slightly better in selenide because CuPb can help
compensate
• Extrinsic defects calculations (not shown) do not provide clear paths forward
Faghaninia A., Yu G., Aydemir U., Wood M., Chen W., Rignanese G.M., Snyder J., Hautier G., Jain, A. A computational assessment
of the electronic, thermoelectric, and defect properties of bournonite (CuPbSbS3) and related substitutions (submitted)
CuPbSbS3 CuPbSbSe3

22. Open data and software
22
www.materialsproject.org
www.pymatgen.org
www.github.com/hackingmaterials/MatMethods
www.pythonhosted.org/FireWorksNote: results of 50,000 transport
calcs will eventually be posted here
Coming soon: AMSET
Coming soon: MatMiner

23. MatMiner (coming soon)
MatMiner’s goal: help enable data mining
studies in materials science
23

24. Interactive demo of MatMiner
• Can we create a machine learning model to
predict bulk modulus that is accurate to
~20GPa in ~10 mins?
• Let’s find out!
• Code posted at:
– https://gist.github.com/computron
24

25. Thank you!
• Collaborating research groups
– Jeffrey Snyder
– Geoffroy Hautier
– Mary Anne White
– Mark Asta
– Hong Zhu
– Kristin Persson
– Gerbrand Ceder
• Primary researchers
– TmAgTe2 – Prof. Hong Zhu and Dr. Umut Aydemir
– YCuTe2 – Dr. Umut Aydemir and Dr. Jan Pohls
– CuPbSbS3 – Dr. Alireza Faghaninia
– MatMiner – Dr. Saurabh Bajaj
• NERSC computing center and staff
• Funding: U.S. Department of Energy, Basic Energy Sciences
25Slides (already) posted to http://www.slideshare.net/anubhavster