Computational screening of tens of thousands of compounds as potential thermoelectrics and their experimental followup This document discusses using computational screening to identify promising thermoelectric materials. It summarizes past successes in predicting materials with high zT values through density functional theory screening of large databases. Two materials identified through screening, TmAgTe2 and YCuTe2, were experimentally synthesized with zT values close to predictions. The document also introduces a new model called AMSET that aims to more accurately calculate electronic transport properties compared to the commonly used BoltzTraP approach with a fixed relaxation time.

1. Computational screening of tens of thousands of
compounds as potential thermoelectrics and their
experimental followup
Anubhav Jain
Energy Technologies Area
Lawrence Berkeley National Laboratory
Berkeley, CA
TMS 2019
Slides (already) posted to hackingmaterials.lbl.gov

2. 2
The experimental community has been steadily finding
diverse, high zT thermoelectric materials

3. Can new computational approaches help find better
thermoelectrics even faster?
As proposed as early as 2003 by Blake and Metiu1:
3
“With the cost of computing become relatively inexpensive one can
envisage a time where one runs multiple computer test tube
reactions like these on large Beowulf clusters - as a means of
screening for new TE materials. Certainly it appears that in the
future theory may be a very competent dance partner for what has
previously been a solo experimental effort in searching for ever
better TE materials.”
1. Blake and Metiu. Can theory help in the search for better thermoelectric materials? Chemistry, Physics,
and Materials Science of Thermoelectric Materials: Beyond Bismuth Telluride, 2003

4. 4
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

5. 5
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

6. Outline
6
① High-throughput DFT-based screening of
thermoelectric materials
② AMSET model: improving the accuracy of
electronic transport calculations

7. 7
Our high-throughput calculation infrastructure
~50,000 crystal
structures and
band structures
from Materials
Project are used
as a source F. Ricci, et al., An ab initio electronic transport
database for inorganic materials, Sci. Data. 4
(2017) 170085.
We compute electronic
transport properties
with BoltzTraP and
minimum thermal
conductivity (Cahill-
Pohl) for some
compounds
About 300GB of
electronic transport
data is generated. All
data is available free
for download.

8. • Advantage – we can screen *many* materials and be quite
comprehensive. ~50,000 materials can be computed and compared.
• Some disadvantages
– Fixed relaxation time often prioritizes materials with flat bands, which is
undesirable in reality
– Properties also overestimated at high temperatures and high doping
– Thermal conductivity numbers are rough estimates
– No modeling of dupability / carrier type in high-throughput
• Thus, we don’t take theory numbers at face value
– e.g., look at the band structure (is it flat bands?)
– does the material require high temperatures or high doping? If so, less
reason to believe we can achieve it in reality
– experimental factors taken into account
– run higher levels of theory, doping, etc.
8
Advantages and disadvantages of approach

9. New Materials from screening – TmAgTe2 (calcs)
9
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
• Calculations:
trigonal p-
TmAgTe2 could
have power
factor up to 8
mW/mK2
• requires 1020/cm3
carriers

10. TmAgTe2 (experiments)
10
1. Zhu, H.; et al. 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
• Expt: p-zT only 0.35 despite
very low thermal
conductivity (~0.25 W/mK)
• Limitation: carrier
concentration (~1017/cm3)
• likely limited by TmAg
defects, as determined by
followup calculations
• Later, we achieved zT ~ 0.47
using Zn-doping
2. Pöhls, J.-H., et al. First-principles calculations and experimental studies of XYZ2 thermoelectric compounds: detailed analysis
of van der Waals interactions. J. Mater. Chem. A 6, 19502–19519. https://doi.org/10.1039/C8TA06470A

11. YCuTe2 – friendlier elements, higher zT (0.75)
11
Aydemir, U.; Pöhls, J.-H.; Zhu, H., 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
• Calculations: p-YCuTe2 could
only reach PF of 0.4
mW/mK2
• SOC inhibits PF
• if thermal conductivity is low
(e.g., 0.4, we get zT ~1)
• Expt: zT ~0.75 – not too far
from calculation limit
• carrier concentration of 1019
• Decent performance, but
unlikely to be improved with
further optimization

12. Outline
12
① High-throughput DFT-based screening of
thermoelectric materials
② AMSET model: improving the accuracy of
electronic transport calculations

13. • As mentioned previously, we cannot take the
BoltzTraP calculations at face value due to the
limitations with constant relaxation time.
• The goal of AMSET is to provide a model that
can explicitly calculate scattering rates while
remaining computationally efficient
13
AMSET is a model to overcome limitations in constant, fixed
relaxation time models
https://github.com/hackingmaterials/amset

14. 14
AMSET overview
• Limitations of AMSET
• Requires distinct band extrema (one or several is fine)
• No intervalley scattering (within band)
• No interband scattering
• No metals

15. 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
15
AMSET scattering equations

16. • AMSET gets many
things correct
– Value of mobility
– Temperature
dependence of mobility
– Predominance of polar
optical scattering across
temperature range
• Note that nothing was
“fit” – everything was
calculated explicitly
16
Example result: n-type GaAs

17. 17
AMSET results – common binary semiconductors
• Here, AMSET essentially provides the accuracy of EPW at ~1/1000 the
computational cost. It can be quite accurate!
• Note that constant relaxation time (BoltzTraP) gives both inaccurate
values of mobility as well as incorrect temperature dependence

18. 18
AMSET results – more complicated electronic structures
• Here, AMSET overpredicts the mobility. This might be a problem in the
underlying DFT-GGA band structure rather than AMSET
• Note that at low temperatures, Ca3AlSb3 might have some defect
scattering not modeled in AMSET.
• Overall, still not bad for a “cheap” model with no fitting parameters!

19. • The next step for AMSET is to run in a “medium”
throughput – i.e., hundreds of compounds
• After that, we can consider potentially thousands
of compounds with relatively accurate electronic
transport properties
• A manuscript is in preparation
19
Next steps

20. • We have screened tens of thousands of compounds
as thermoelectrics using the BoltzTraP level of
theory
– About 300GB of data on 50,000 materials is available
online
• Two materials (TmAgTe2 and YCuTe2) were
experimentally synthesized
• We are developing a new level of theory called
AMSET that gives more accurate results
20
Conclusions

21. • Thermoelectrics screening
– G. Snyder, G. Hautier, M.A. White, U. Aydemir, J. Pohls, G. Ceder,
& many others on the team
• AMSET
– A. Faghaninia and A. Ganose
• Funding:
– U.S. Department of Energy, Basic Energy Sciences, Early Career
Research Program
• Computing: NERSC
21
Thank you!
Slides (already) posted to hackingmaterials.lbl.gov