This work was supported by funding from the U.S. Department of Energy and involved collaborations with researchers from Northwestern University, Universite Catholique de Louvain, Dalhousie University, and UC Berkeley/LBNL. Using high-throughput computational methods, over 50,000 compounds were screened to discover new thermoelectric materials, identifying YCuTe2 as a promising candidate with a calculated zT of 0.75. Experimental synthesis and testing achieved a maximum zT of 0.75 for this material. Future work will involve developing more accurate electronic transport models, expanding the materials library through substitutional predictions, and applying machine learning to discover new structure-property relationships.

1. External collaborators and funding
This work was supported by the U.S. Department of Energy, Basic Energy
Sciences, Early Career Program.
Collaborators: Northwestern University (Snyder lab), Universite Catholique de
Louvain (Hautier Lab), Dalhousie University (White Lab), and UC Berkeley /
LBNL (Ceder, Asta, and Persson labs).
Targeted Band Structure Design and Thermoelectric Materials Discovery
Using High-Throughput Computation
Anubhav Jain, Energy Technologies Area, Berkeley Lab
High-throughput calculation engine and
electronic transport database
Data mining thermoelectrics and band
structure engineering
Benchmarking and accuracy
Seebeck coefficient vs. electrical
conductivity (colored by power factor)
over the transport database (50,000+
compounds) computed with DFT / GGA
band structures & BoltzTraP under a
constant relaxation time approximation.
We have developed a state-of-the-art, open-
source calculation engine for performing high-
throughput calculations:
• FireWorks workflow software
• MatMethods automatic calculation recipes
All software is available open-source at
www.github.com/hackingmaterials
We computed a database of electronic
transport properties (50,000 compounds)
using BoltzTraP (cRTA) and applied it to
thermoelectrics discovery. The complete
transport database results will soon be
disseminated publicly.
Potential sources of error in the
calculations include well-known problems
with DFT/GGA band gaps, band curvature
and DOS inaccuracies, and the use of a
constant relaxation time approximation
(cRTA). We compared our computations
with available experimental data to
determine that:
• DFT underestimation of small band
gaps is a major component of Seebeck
coefficient errors; a “scissor” operation
improves results but requires knowledge
of the band gap or use of higher-order
computational techniques.
• Errors in power factor likely stem from a
constant relaxation time approximation.
For small gap compounds, band
curvature differences also contribute.
A collaborative effort examined the
electronic transport database for novel
thermoelectrics candidates. We targeted
YCuTe2 due to (i) reasonable calculated
thermoelectric figure-of-merit, (ii)
generally low thermal conductivities of
copper chalcogenides and potential for
liquid-like Cu phonon scattering
mechanisms, and (iii) a good match to the
synthesis capabilities of the Snyder
research group.
New thermoelectric materials screening:
the case of YCuTe2 (zT ~ 0.75)
Future work – beyond constant relaxation time
We are developing a new method for electronic transport from first-
principles that includes multiple scattering mechanisms and shows
much better agreement with experiment than BoltzTraP cRTA. A
software implementation, AMSET, will be made available in 2017.
Future work – chemical substitution strategy
The current library of ~50,000 compounds is derived from compounds
contained in the Materials Project (MP) database. Next, we will initiate
chemical substitutions into MP compounds to generate completely
novel materials for thermoelectrics and other applications.
Future work – relating structure and band structure
We are building a set of compositional, structural, and band structure
descriptors that will serve as physically-motivated “features” for
machine learning algorithms. The overall goal is to build data-driven
models that provide mechanistic insights into band structure formation.
Comparisons of calculated versus
experimental values for Seebeck
coefficient (top) and power factor
(bottom). Data set includes undoped
(filled) and doped (open) compounds; red
circles indicate that the sign of
computation and experiment differ.
Low- and high-temperature (>440K)
crystal structures of YCuTe2. Cu
positions are disordered in the HT phase.
Projected GGA band structure (top-left)
and spin-orbit band structure (bottom-
left) of YCuTe2. Right: Theoretical zT as
a function of Fermi level.
Further calculations revealed that
achievable figure-of-merit (zT) is reduced
by spin-orbit coupling, which removes a
band degeneracy at the VBM Γ point (Te
5p character). However, a low theoretical
minimum thermal conductivity of 0.43 W
m-1 K-1 calculated using the Cahill-Pohl
model (glassy limit) encouraged us to
continue exploring this material.
Experimentally measured zT of several
YCuTe2 derivatives synthesized off-
stoichiometry. Performance of a
previously known TmCuTe2 phase, with
a different structure but similar
performance, is shown for reference.
Experiments carried out by the Snyder
(Northwestern) and White (Dalhousie
University) groups revealed a zT
reaching as high as 0.75. The
moderately high zT stems from a very
low measured thermal conductivity of
0.5 W m-1 K-1, very close to the
calculated minimum. Increasing the
carrier concentration beyond 1019
should further increase the zT of this
material (~1.0), but our attempts to
extrinsically dope this compound have
thus far been unsuccessful.
Computational studies are capable of
generating large data sets and offer
independent control over many variables.
These strengths should make it possible
to use calculations to extract the chemical
and structural factors that lead to specific
electronic properties.
It is well known that oxide thermoelectrics
in general exhibit lower zT than other
chemistries. Possible reasons include
higher thermal conductivity (due to the
lower anion weight of oxygen), poor
dopability of oxides (due to larger band
gaps), and shorter relaxation times. Our
computational database indicates that,
even after controlling for all of these
factors, oxides still show significantly
lower zT than other anions, indicating
band shape as a further differentiator.
We have begun building data mining
models to predict the band structure
features of hypothetical compounds. One
model predicts the orbital character of the
VBM and CBM from the chemical
components of a compound, which helps
tailor chemical substitutions that retain or
introduce a particular band character.
Violin plots depicting the distribution of
computed maximum power factor under
a fixed relaxation time approximation
across several thousand compounds,
separated by anion type. The red lines
indicate the median computed power
factor for each anion.
Portion of a pairwise probability diagram
to predict the CBM character of
compounds. Blue points indicate that the
ionic orbital type on the y-axis is likely to
dominate the CBM over the ionic orbital
type on the x-axis; the opposite is true for
red-points. As a simple example, in a
compound containing both V4+ and
N3-, the odds are very high that V4+ will
contribute more to the CBM than N3-.