Poster given at the MGI PI meeting (College Park, MD), March 26-27, 2018

1. MATERIAL FEATURES PROPERTY
TiO2 rutile F11 F12 … F1N gap = 3.0 eV
C diamond F21 F22 … F2N gap = 5.5 eV
… … … … … …
PbTe rocksalt FM1 FM2 … FMN gap = 0.3 eV
Python
ML Libraries
Data
Featurization
Data
Retrieval
Data
Visualization
Materials Databases
MPDSCitrine
Materials
Project
Software tools Materials Project integration
Software tools for data-driven research and
their application to thermoelectrics materials discovery
Anubhav Jain, Energy Storage & Distributed Resources Division, Berkeley Lab
Thermoelectrics discovery
Atomate is a library of
standardized workflows for
high-throughput computational
materials science. One can
provide a crystal structure and
select from >15 different types
of calculation procedures (e.g.,
band structure, elastic tensor,
thermal expansion, etc.) to
perform. Atomate scales to
millions of calculations at large
supercomputing centers and
produces a database of
organized results.
www.github.com/hackingmaterials/atomate
(production)
Matminer retrieves data from
the APIs of several large
databases and assists the user
with feature extraction,
implementing over 20 different
featurization classes that can
g e n e r a t e t h o u s a n d s o f
physically-relevant materials
descriptors. Matminer includes
a built-in visualization package
and interfaces to standard
Python-based libraries for
machine learning.
www.github.com/hackingmaterials/matminer
(released)
The AMSET project develops
and implements an ab initio
model for electron transport
(mobility, Seebeck) that
b a l a n c e s a c c u r a c y a n d
computational efficiency. It
achieves this by adapting
classical scattering equations
intended for single 1D
parabolic bands to complex,
anisotropic band structures
c o m p u t e d f ro m d e n s i t y
functional theory methods.
www.github.com/hackingmaterials/amset
(in development)
The Materials Project produces
computationally-generated
m a t e r i a l s d a t a t h a t i s
disseminated to over 45,000
registered users. The software
tools developed in our project
are integrated with the
Materials Project. For example,
the atomate software will be
used by Materials Project to run
millions of simulations in the
upcoming year.
We have recently developed a set
of order parameters to characterize
the local environment of a site in a
structure. Order parameter
functions produce a value of 1 when
matching a target geometry (e.g.,
“tetrahedral”) and continuously
fade to zero as the neighbor
geometry deviates from the target.
The set of order parameters are
assembled into a “fingerprint” that
characterizes a site’s resemblance to
~20 coordination motifs. The site
fingerprints are then assembled into
a structure fingerprint.
One application deployed on MP is
to compute “similarities” between
crystal structures in terms of
fingerprint distance, which is robust
even under distortion or alloying.
We used the atomate library to
compute the electron transport
properties of ~48,000 inorganic
materials using the BoltzTraP
method under a constant
relaxation time approximation.
This database (~300GB) is
downloadable online through
the Dryad repository and is
used in our project as the basis
for identifying potential new
thermoelectric compositions.
One novel thermoelectric
composition identified from the
computational screening is p-
type YCuTe2, which exhibits
favorable band convergence
and exhibits a peak zT of ~0.75
(in-line with computational
estimates). To our knowledge,
this is the highest experimental
zT from a composition first
suggested from computational
guidance. Another material,
TmAgTe2, was suggested but
its experimental zT (~0.35) was
limited by doping issues.
AMSET!
>45,000
users!
Target: W
similar structures
detected
Cs3Sb!
TiGaFeCo!
CeMg2Cu!
www.materialsproject.org/materials/mp-91/
1.0E+02
1.0E+03
1.0E+04
1.0E+05
1.0E+06
1.0E+07
1.0E+08
200 300 400 500 600 700 800 900 1000 1100
Mobility(cm2/V*s)
Temperature (K)
IMP
PIE
ACD
POP
overall
expt
experiment
computation
We have also investigated
traditionally under-explored
chemistries such as phosphide
thermoelectrics. Contrary to
the belief that phosphides
should have high thermal
c o n d u c t i v i t y ( κ ) , o u r
computations suggest that the
m i n i m u m κ o f t h e s e
compounds can be quite low.
Experimental tests of cubic
NiP2 confirms low κ (~1.0 W/
m*K) but with poor electronic
properties.
Funding
Funding for this research was provided by the U.S.
Department of Energy, Basic Energy Sciences, Materials
Science Division through an Early Career Grant and through
the Materials Project center grant EDCBEE. Computing
resources were provided by the National Energy Research
Scientific Computing Center.
Registered users over time
www.materialsproject.org
Electron mobility of n-type GaAs
(ne = 3.0 * 1013 cm-3)