Presentation given at MRS Spring 2019

1. Combined Theory and Data-Driven Approaches
to Thermoelectrics Materials Discovery
Anubhav Jain
Energy Technologies Area
Lawrence Berkeley National Laboratory
Berkeley, CA
MRS Spring 2019
Slides (already) posted to hackingmaterials.lbl.gov

2. 2
Today, it is possible to screen for thermoelectric materials
computationally
Year Composition Method of prediction Peak zT in experiments Notes
2006 -
2009
LiZnS DFT-based screening of 570
Sb-containing
0.08 at ~525 K, p-type Could not be doped n-
type
2008 -
2015
NbFeS DFT based screening of 36
half-Heusler compositions
1.5 at 1200 K, p-type Multiple independent
predictions
2014 SnS High-throughput screening
>450 binary sulfides
0.6 at 873 K, p-type Complex prediction
history
2015 TmAgTe2 DFT-based screening of
~48,000 compounds
0.47 at ~700 K, p-type Couldn’t dope to
desired carrier
concentration
2016 YCuTe2 Substitutions from above
screening
0.75 at 780 K, p-type Experiment is close to
prediction (zT ~0.82)
2016 Er12Co5Bi Machine learning
recommendation engine
0.07 at 600 K, n-type Pure ML, no theory
2017 KAlSb4 DFT-based screening of 145
Zintl compounds
0.7 at ~650 K, n-type Experiment is very
close to prediction
2018 Cd1.6Cu3.4In3Te8 DFT-based screening of 214
diamond-like systems
1.04 at 875 K, p-type CdIn2Te4 was the initial
hit from screening
2019 TaFeSb DFT-based screening of 27
half-Heusler compounds
1.52 at 973 K, p-type Compound never
reported previously
Urban, Menon, Tian, Jain, Hippalgoankar. New Horizons in Thermoelectric Materials…in review, J. Applied Physics

3. 3
The record so far in terms of computationally-guided
thermoelectrics predictions
Year Composition Method of prediction Peak zT in experiments Notes
2006 -
2009
LiZnS DFT-based screening of 570
Sb-containing
0.08 at ~525 K, p-type Could not be doped n-
type
2008 -
2015
NbFeS DFT based screening of 36
half-Heusler compositions
1.5 at 1200 K, p-type Multiple independent
predictions
2014 SnS High-throughput screening
>450 binary sulfides
0.6 at 873 K, p-type Complex prediction
history
2015 TmAgTe2 DFT-based screening of
~48,000 compounds
0.47 at ~700 K, p-type Couldn’t dope to
desired carrier
concentration
2016 YCuTe2 Substitutions from above
screening
0.75 at 780 K, p-type Experiment is close to
prediction (zT ~0.82)
2016 Er12Co5Bi Machine learning
recommendation engine
0.07 at 600 K, n-type Pure ML, no theory
2017 KAlSb4 DFT-based screening of 145
Zintl compounds
0.7 at ~650 K, n-type Experiment is very
close to prediction
2018 Cd1.6Cu3.4In3Te8 DFT-based screening of 214
diamond-like systems
1.04 at 875 K, p-type CdIn2Te4 was the initial
hit from screening
2019 TaFeSb DFT-based screening of 27
half-Heusler compounds
1.52 at 973 K, p-type Compound never
reported previously
Urban, Menon, Tian, Jain, Hippalgoankar. New Horizons in Thermoelectric Materials…in review, J. Applied Physics

4. Outline
4
① AMSET model: improving the accuracy of
electronic transport calculations
② Suggesting new thermoelectrics by
“reading the literature” using natural
language processing

5. • High-throughput calculations of mobility (and
Seebeck) typically employ a constant, fixed
relaxation time approximation
• The goal of AMSET is to provide a model that
can explicitly calculate scattering rates while
remaining computationally efficient
– E.g., the accuracy of EPW at 1/1000 the
computational cost
5
AMSET is a model to overcome limitations in constant, fixed
relaxation time models
https://github.com/hackingmaterials/amset

6. 6
AMSET overview

7. 7
AMSET overview
• Limitations of AMSET
• Requires distinct band extrema (one or several is fine)
• No intervalley scattering (two valleys within the same band)
• No interband scattering (two valleys in different bands)
• No metals (need distinct VB and CB)
• Anisotropy is OK! (but takes more time)

8. Acoustic deformation potential
scattering (ADP)
Inputs: Deformation potential, elastic constant
Ionized impurity scattering (IMP)
Inputs: Dielectric constant
Piezoelectric scattering (PIE)
Inputs: Dielectric constant, piezoelectric
coefficient
Polar optical phonon scattering (POP)
Inputs: Polar optical phonon frequency,
dielectric constant
8
AMSET scattering equations

9. 9
AMSET mobility (no fitting parameters) and comparison
against cRTA
Paper in preparation
Anisotropic-
b-axis data

10. 10
AMSET Seebeck results (no fitting parameters)
Paper in preparation

11. • The next step for AMSET is to run in a “medium”
throughput – i.e., hundreds of compounds
• We also want to auto-detect when AMSET might
not be applicable
– likely to have intervalley / interband scattering
– can’t separate band structure into distinct valleys
• A manuscript is in preparation
• https://github.com/hackingmaterials/amset/
11
Next steps

12. Outline
12
① AMSET model: improving the accuracy of
electronic transport calculations
② Suggesting new thermoelectrics by
“reading the literature” using natural
language processing

13. We have extracted ~3
million abstracts of
scientific articles
We will use natural
language processing
algorithms to try to
extract knowledge from
all this data
13
Do past journal articles contain enough information to
predict what materials will be studied in the future?

14. • We use the word2vec
algorithm (Google) to turn
each unique word in our
corpus into a 200-
dimensional vector
• These vectors encode the
meaning of each word
meaning based on trying to
predict context words
around the target
14
Key concept 1: the word2vec algorithm
Paper in review

15. • Dot product of a composition word
with the word “thermoelectric”
essentially predicts how likely that
word is to appear in an abstract with
the word thermoelectric
• Compositions with high dot products
are typically known thermoelectrics
• Sometimes, compositions have a high
dot product with “thermoelectric” but
have never been studied as a
thermoelectric
• These compositions usually have high
computed power factors! (BoltzTraP)
15
Key concept 2: vector dot products measure similarity
Paper in review

16. “Go back in time”
approach:
– For every year since
2001, see which
compounds we would
have predicted using only
literature data until that
point in time
– Make predictions of what
materials are the most
promising thermoelectrics
for data until that year
– See if those materials
were actually studied as
thermoelectrics in
subsequent years 16
Can we predict future thermoelectrics discoveries with this
method?
Paper in review

17. • Thus far, 2 of our top 20 predictions made in
~August 2018 have already been reported in the
literature for the first time as thermoelectrics
– Li3Sb was the subject of a computational study
(predicted zT=2.42) in Oct 2018
– SnTe2 was experimentally found to be a moderately
good thermoelectric (expt zT=0.71) in Dec 2018
17
How about “forward” predictions?
[1] Yang et al. "Low lattice thermal conductivity and
excellent thermoelectric behavior in Li3Sb and Li3Bi."
Journal of Physics: Condensed Matter 30.42 (2018):
425401
[2] Wang et al. "Ultralow lattice thermal conductivity and
electronic properties of monolayer 1T phase semimetal
SiTe2 and SnTe2." Physica E: Low-dimensional Systems and
Nanostructures 108 (2019): 53-59

18. • We are developing a new level of theory called
AMSET that gives more accurate results for
mobility / Seebeck at low computational cost
– https://github.com/hackingmaterials/amset/
• We are employing text mining to suggest
compositions likely to be thermoelectrics
18
Conclusions

19. • AMSET
– A. Faghaninia and A. Ganose
– Funding: U.S. Department of Energy, Basic Energy Sciences, Early
Career Research Program
– Computing: NERSC
• Text mining
– V. Tshitoyan, J. Dagdelen, L. Weston, K.A. Persson, G. Ceder
– Funding: Toyota Research Institute
19
Thank you!
Slides (already) posted to hackingmaterials.lbl.gov

20. 20
Interpreting predictions

21. 21
AMSET mobility results (no fitting parameters)

22. 22
AMSET mobility results (no fitting parameters)
Overestimation
due to lack of
intervalley
scattering

23. 23
AMSET mobility (no fitting parameters) and comparison
against cRTA – constant temperature (T=300K)
Paper in preparation