Density functional theory calculations and data
mining for new thermoelectrics discovery
Anubhav Jain
Energy Technologies Area
Lawrence Berkeley National Laboratory
Berkeley, CA
2018 Conference on Electronic & Advanced Materials
Slides (already) posted to http://www.slideshare.net/anubhavster

Outline
• Using high-throughput density functional theory
to screen for new thermoelectric candidates
– successes and failures
• Open-source software tools for data generation
and data mining
– atomate to easily run high-throughput DFT
– matminer to help in materials data mining
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)
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www.alphabetenergy.com
Alphabet Energy – 25kW generator

Thermoelectric figure of merit
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• Many materials properties are important for thermoelectrics
• Focus is usually on finding 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!

Example: Seebeck and e– conductivity tradeoff
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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!
E
k

We’ve initiated a search for new bulk thermoelectrics
6
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).

Database of transport properties calculated
7
All data (~300GB total) is
available for direct download
through the Dryad repository
linked in the following
publication:
F. Ricci, W. Chen, U. Aydemir, G.J. Snyder, G.-M.
Rignanese, A. Jain, et al., An ab initio electronic
transport database for inorganic materials, Sci.
Data. 4 (2017) 170085.

New Materials from screening – TmAgTe2 (calcs)
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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

TmAgTe2 (experiments)
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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
• 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

YCuTe2 – friendlier elements, higher zT (0.75)
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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

Bournonites – CuPbSbS3 and analogues (no expt)
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A. Faghaninia, G. Yu, U. Aydemir, M. Wood, W. Chen, G.-M.G.-M. Rignanese, et al., A computational assessment of the electronic,
thermoelectric, and defect properties of bournonite (CuPbSbS3) and related substitutions, Phys. Chem. Chem. Phys. 19 (2017)
6743–6756.
• Previously studied TE material
– Measured thermal conductivity < 1 W/m*K
– Measured Seebeck coefficient ~ 400 µV/K
– BUT electrical conductivity requires improvement – can
calculations help?
• Calculations: p-PF is 13.8, but we know
electronic conductivity will be lower than
estimated
– Try ~320 chemical substitutions, see whether
electronic scattering time is reduced in
computational models or whether favorable defect
diagram can be found for high carrier concentration
– Computations suggest a few interesting candidates,
including CuPbSnSe3 and CuPbAsSe3
• Experiments: Preliminary experiments are
unsuccessful in synthesizing bournonite
CuPbSnSe3. Paused investigations due to time
constraints.

1-2-1-4 selenides: poor electrical conductivity
12
Kuo et al, Low Thermal Conductivity
Selenide Compounds, in preparation
Se
Ba
Ag
Sn
• Calculations:
– p-PFs in the range of 9 – 17.3
assuming a carrier concentration of
1020/cm3
– Calculations indicate low thermal
conductivity, possibly due to Ag-Ag
dimers
• Experiments:
– Thermal conductivity is indeed
very low
– But carrier concentrations and
conductivity are much too low –
not a viable thermoelectric

Thermoelectrics screening: lessons so far
• When considering our screening strategy in the abstract, the
major limitations appeared to be:
– no modeling of electron relaxation time
– limited modeling of thermal conductivity
• However, in reality the biggest limitation has been estimating
dopability. This has been the major limitation for all our picks.
– The materials we pick just aren’t very dopable for some reason
– Computing doping limits is hard; we also learned not to trust GGA
defect diagrams for this purpose (at least shift the band edges with
an HSE band gap estimate)
• So the problems are often not in the known limitations of the
theory, but in optimizing aspects of the material you are not
computing at all
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Outline
• Using high-throughput density functional theory
to screen for new thermoelectric candidates
– successes and failures
• Open-source software tools for data generation
and data mining
– atomate to easily run high-throughput DFT
– matminer to help in materials data mining
14

A “black-box” view of performing a calculation
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“something”!
Results!!
researcher!
What is the
GGA-PBE elastic
tensor of GaAs?

Unfortunately, the inside of the “black box” is usually
tedious and “low-level”
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lots of tedious,
low-level work…!
Results!!
researcher!
What is the
GGA-PBE elastic
tensor of GaAs?
Input file flags
SLURM format
how to fix ZPOTRF?

Atomate tries make it easy, automatic, and flexible (worry
only about the things you want to worry about)
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Results!!
researcher!
What is the
GGA-PBE elastic
tensor of GaAs?

Atomate knows the sequence of calculations needed to
compute many kinds of materials properties
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quickly and automatically translate PI-style (minimal)
specifications into well-defined FireWorks workflows
What is the
GGA-PBE elastic
tensor of GaAs?
M. De Jong, W. Chen, T. Angsten, A. Jain, R. Notestine, A. Gamst, et al.,
Charting the complete elastic properties of inorganic crystalline compounds,
Sci. Data. 2 (2015).

Many types of simulation procedures are already available!
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• band structure
• BoltzTraP transport
• spin-orbit coupling
• hybrid functional calcs
• elastic tensor
• piezoelectric tensor
• Raman spectra
• NEB
• GIBBS method
• QH thermal expansion
• AIMD
• ferroelectric properties
• (defects upcoming?)
• FEFF method
• LAMMPS MD
Workflows currently available in atomate!
Mathew, K. et al Atomate: A high-level interface to generate,
execute, and analyze computational materials science workflows,
Comput. Mater. Sci. 139 (2017) 140–152.

Workflow parameters can be customized at
multiple levels of detail
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1. Workflows have
various high-level
options
2. Fireworks also
have options / flags
(not shown)
3. Firetasks have
most detailed
number of options /
flags (not shown)
Example 1: “VASP input set”
controls the rules that set DFT
parameters (pseudopotentials,
cutoffs, grid densities, etc) via
pymatgen!
!
Example II: If “stability_check” is
enabled, the later parts of the
workflow are skipped if the
structure is determined unstable to
save computer time on
uninteresting structures!
Mathew, K. et al Atomate: A high-
level interface to generate, execute,
and analyze computational
materials science workflows,
Comput. Mater. Sci. 139 (2017)
140–152.

atomate allows you to leverage the prior efforts and
knowledge of many researchers
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K. Mathew J. Montoya S. Dwaraknath A. Faghaninia
All past and present knowledge, from everyone in the group,
everyone previously in the group, and our collaborators,
about how to run calculations
M. Aykol
S.P. Ong
B. Bocklund T. Smidt
H. Tang I.H. Chu M. Horton J. Dagdalen B. Wood
Z.K. Liu J. Neaton K. Persson A. Jain
+

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Many research groups have run tens of thousands of
materials science workflows with atomate
also used by:
• Persson research group, UC Berkeley
• Ong research group, UC San Diego
• Neaton research group, UC Berkeley
• Liu research group, Penn State
atomate now powers the
Materials Project

Machine learning: the big problem in my view is connecting
data to ML algorithms through features
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Lots of data on
complex objects that
you want to interrelate
Clustering, Regression, Feature
extraction, Model-building, etc.
Well developed
data-mining routines that work
only on numbers (ideally ones
with high relevance to your
problem)
Need to transform materials science objects into a set of
physically relevant numerical data (“features” or “descriptors”)

Goal of matminer: connect materials data with data mining
algorithms and data visualization libraries
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Materials databases (e.g.,
Materials Project, Citrine, MPDS)!
Data retrieval
Data
featurization
Data
visualization
Python ML
libraries

Matminer contains a library of descriptors (developed by us
and the community) to use for data mining
• Composition-based
– various combinations of elemental attributes of the composition
• electronegativity, atomic/ionic radii, elemental thermal conductivities,
etc – hundreds of these properties are available
– Miedema model
– Atomic orbital energies and associated HOMO/LUMO states
• Structure-based
– structure fingerprints (e.g., site fingerprint statistics), Coulomb
matrix, Orbital Field Matrix, RDF-based metrics, Coordination
number based metrics
• Site-based
– Order parameters, AGNI, Voronoi indices, etc
• Band structure / DOS based
– Branch point energy, orbital character at VBM/CBM, band
structure “fingerprint”, etc…
25

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An example of using structure-based fingerprints to find
similar structures in terms of local environment patterns
Order parameter type
Distance
between
LOPs
statistics
vector d LOP
TARGET
CuBiSeO
Rocksalt (dissimilar)
similar structures

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How to try out these codes
https://github.com/hackingmaterials/atomate
(production ready)
(beta release)
https://github.com/hackingmaterials/matminer

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
– 1-2-1-4 selenides – Jimmy Kuo
– atomate – Dr. Kiran Mathew
– MatMiner – Dr. Logan Ward
• NERSC computing center and staff
• Funding: U.S. Department of Energy, Basic Energy Sciences
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Slides (already) posted to http://www.slideshare.net/anubhavster