Presentation given at ETA Early Career Enrichment Program, Oct 2018

1. Materials discovery through theory,
computation, and machine learning
Anubhav Jain
Energy Technologies Area
Lawrence Berkeley National Laboratory
Berkeley, CA
Oct 10, 2018
Slides (already) posted to hackingmaterials.lbl.gov

2. 2
Materials and their properties decide what is
technologically possible
Example: Electric vehicles and solar power are two
technologies that have been dreamed about for many
decades that are finally starting to see large-scale adoption.
Most of the “waiting” has been for materials improvements!

3. What constrains traditional approaches to materials design?
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“[The Chevrel] discovery resulted from a lot of
unsuccessful experiments of Mg ions insertion
into well-known hosts for Li+ ions insertion, as
well as from the thorough literature analysis
concerning the possibility of divalent ions
intercalation into inorganic materials.”
-Aurbach group, on discovery of Chevrel cathode
for multivalent (e.g., Mg2+) batteries
Levi, Levi, Chasid, Aurbach
J. Electroceramics (2009)

4. Density functional theory (DFT) can model properties of
materials from first principles
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• 1920s: The Schrödinger equation for quantum mechanics essentially contains
all of chemistry embedded within it
• it is almost always too complicated to solve due to the numerous electron
interactions and complexity of the wave function entity
• 1960s: DFT is developed and reframes the problem for ground state
properties of the system to separate interactions and written in terms of the
charge density, not wavefunction
• makes solutions tractable while in principle not sacrificing accuracy for
the ground state!
e–
e– e–
e– e–
e–

5. How does one use DFT to design new materials?
5
A. Jain, Y. Shin, and K. A.
Persson, Nat. Rev. Mater.
1, 15004 (2016).

6. We developed a way to automate DFT on supercomputers
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Automate the DFT
procedure
Supercomputing
Power
FireWorks
Software for programming
general computational
workflows that can be
scaled across large
supercomputers.
NERSC
Supercomputing center,
processor count is
~100,000 desktop
machines. Other centers
are also viable.
High-throughput
materials screening
G. Ceder & K.A.
Persson, Scientific
American (2015)

7. Computations predict, experiments confirm
7
Sidorenkite-based Li-ion battery
cathodes
YCuTe2 thermoelectrics
Chen, H.; Hao, Q.; Zivkovic, O.; Hautier, G.; Du, L.-S.; Tang,
Y.; Hu, Y.-Y.; Ma, X.; Grey, C. P.; Ceder, G. Sidorenkite
(Na3MnPO4CO3): A New Intercalation Cathode Material
for Na-Ion Batteries, Chem. Mater., 2013
Aydemir, U; Pohls, J-H; Zhu, H; Hautier, G; Bajaj, S; Gibbs,
ZM; Chen, W; Li, G; Broberg, D; White, MA; Asta, M;
Persson, K; Ceder, G; Jain, A; Snyder, GJ. Thermoelectric
Properties of Intrinsically Doped YCuTe2 with CuTe4-based
Layered Structure. J. Mat. Chem C, 2016
More examples here: A. Jain, Y. Shin, and K. A. Persson, Nat. Rev. Mater. 1, 15004 (2016).
Li-M-O CO2 capture compounds
Dunstan, M. T., Jain, A., Liu, W., Ong, S. P., Liu, T., Lee,
J., Persson, K. A., Scott, S. A., Dennis, J. S. & Grey, C. .
Energy and Environmental Science (2016)

8. Putting the data online - Materials Project database
• Online resource of density
functional theory simulation data
for ~85,000 inorganic materials
• Includes band structures, elastic
tensors, piezoelectric tensors,
battery properties and more
• >60,000 registered users
• Free
• www.materialsproject.org
8
Jain et al. Commentary: The Materials Project: A
materials genome approach to accelerating
materials innovation. APL Mater. 1, 11002 (2013).!

9. 9
We also release open-source software libraries for high-
throughput computation and materials science
Data generation Data analysis
run and manage millions of computational
tasks over large computing resources
library of FireWorks-compatible workflows
for materials science applications
materials data retrieval, featurization,
and visualization for machine learning
tools for crystal manipulation, data
analysis, and simulation software I/O
*led by Ong group, UCSD
tools for inverse optimation / adaptive design –
ML chooses what calculations to run

10. 10
An engine to label the content of scientific abstracts
Collect, clean, and extract information from millions of
published materials science journal abstracts

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Text mining to predict new materials in advance
Top predictions from machine learning, trained
on prior years, predict research in future years
Note: each year is trained only on abstracts published until that year

12. • Materials Project
– K. Persson (director)
• Text mining
– V. Tshitoyan, J. Dagdelen, L. Weston
• Funding:
– DOE-BES (MP)
– DOE-BES (ECRP)
– Toyota Research Institute
• Computing: NERSC
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Thank you!
Slides (already) posted to hackingmaterials.lbl.gov