The document summarizes how computational materials science using density functional theory (DFT) calculations, supercomputing, and data-driven methods can help design new materials faster than traditional experimental approaches. It describes how high-throughput DFT calculations are run on supercomputers to screen large numbers of potential materials. The results are compiled in open databases like the Materials Project to be shared and reused by researchers. While computational limitations remain, combining computation and data is helping accelerate the discovery of new materials with improved properties for applications like batteries, thermoelectrics, and carbon capture.

1. Combining density functional theory
calculations, supercomputing, and data-driven
methods to design new materials
Anubhav Jain
Energy Technologies Area
Lawrence Berkeley National Laboratory
Berkeley, CA
Slides posted to http://www.slideshare.net/anubhavster

2. New materials discovery for devices is needed but sporadic
• Novel materials with enhanced performance characteristics
could make a big dent in sustainability, scalability, and cost
• In practice, we tend to re-use the same fundamental
materials for decades
– solar power w/Si since 1950s
– graphite/LiCoO2 (basis of today’s Li battery electrodes) since
1990
• Obviously, there are lots of improvements to manufacturing,
microstructure, etc., but how about new basic compositions?
• Why is discovering better materials such a challenge?
2

3. What constrains traditional experimentation?
3
“[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. Can we invent other, faster ways of finding materials?
• The Materials Genome
Initiative thinks it is possible to
“discover, develop,
manufacture, and deploy
advanced materials at least
twice as fast as possible
today, at a fraction of the
cost”
• Major components of the
strategy include:
– simulations & supercomputers
– digital data and data mining
– better merging computation
and experiment
4
https://obamawhitehouse.archives.gov/mgi

5. Outline
5
① Intro to Density Functional Theory (DFT)
② The Materials Project database
③ Next steps

6. An overview of materials modeling techniques
6
Source: NASA

7. What is density functional theory (DFT)?
7
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DFT is a method to solve for the electronic structure and energetics of
arbitrary materials starting from first-principles.
In theory, it is exact for the ground state. In practice, accuracy depends on the
choice of (some) parameters, the type of material, the property to be studied,
and whether the simulated crystal is a good approximation of reality.
DFT resulted in the 1999 Nobel Prize for chemistry (W. Kohn). It is
responsible for 2 of the top 10 cited papers of all time, across all sciences.

8. How does one use DFT to design new materials?
8
A. Jain, Y. Shin, and K. A.
Persson, Nat. Rev. Mater.
1, 15004 (2016).

9. How accurate is DFT in practice?
9
Shown are typical DFT results for (i) Li
battery voltages, (ii) electronic band gaps,
and (iii) bulk modulus
(i) (ii)
(iii)
(i) V. L. Chevrier, S. P. Ong, R. Armiento, M. K. Y. Chan, and G. Ceder,
Phys. Rev. B 82, 075122 (2010).
(ii) M. Chan and G. Ceder, Phys. Rev. Lett. 105, 196403 (2010).
(iii) M. De Jong, W. Chen, T. Angsten, A. Jain, R. Notestine, A. Gamst,
M. Sluiter, C. K. Ande, S. Van Der Zwaag, J. J. Plata, C. Toher, S.
Curtarolo, G. Ceder, K.A. Persson, and M. Asta, Sci. Data 2, 150009
(2015).

10. Outline
10
① Intro to Density Functional Theory (DFT)
② The Materials Project database
③ Next steps

11. High-throughput DFT: a key idea
11
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)

12. Examples of (early) high-throughput studies
12
Application Researcher Search space Candidates Hit rate
Scintillators Klintenberg et al. 22,000 136 1/160
Curtarolo et al. 11,893 ? ?
Topological insulators Klintenberg et al. 60,000 17 1/3500
Curtarolo et al. 15,000 28 1/535
High TC superconductors Klintenberg et al. 60,000 139 1/430
Thermoelectrics – ICSD
- Half Heusler systems
- Half Heusler best ZT
Curtarolo et al. 2,500
80,000
80,000
20
75
18
1/125
1/1055
1/4400
1-photon water splitting Jacobsen et al. 19,000 20 1/950
2-photon water splitting Jacobsen et al. 19,000 12 1/1585
Transparent shields Jacobsen et al. 19,000 8 1/2375
Hg adsorbers Bligaard et al. 5,581 14 1/400
HER catalysts Greeley et al. 756 1 1/756*
Li ion battery cathodes Ceder et al. 20,000 4 1/5000*
Entries marked with * have experimentally verified the candidates.
See also: Curtarolo et al., Nature Materials 12 (2013) 191–201.

13. Computations predict, experiments confirm
13
Sidorenkite-based Li-ion battery
cathodes
Carbon capture
YCuTe2 thermoelectrics
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.
Large scale computational screening and experimental
discovery of novel materials for high temperature CO2
capture. Energy and Environmental Science (2016)
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).

14. Another key idea: putting all the data online
14
Jain*, Ong*, Hautier, Chen, Richards, Dacek, Cholia, Gunter, Skinner, Ceder,
and Persson, APL Mater., 2013, 1, 011002. *equal contributions
The Materials Project (http://www.materialsproject.org)
free and open
~30,000 registered users
around the world
>65,000 compounds
calculated
Data includes
• thermodynamic props.
• electronic band structure
• aqueous stability (E-pH)
• elasticity tensors
• piezoelectric tensors
>75 million CPU-hours
invested = massive scale!

15. The data is re-used by the community
15
K. He, Y. Zhou, P. Gao, L. Wang, N. Pereira, G.G. Amatucci, et al.,
Sodiation via Heterogeneous Disproportionation in FeF2 Electrodes for
Sodium-Ion Batteries., ACS Nano. 8 (2014) 7251–9.
M.M. Doeff, J. Cabana,
M. Shirpour, Titanate
Anodes for Sodium Ion
Batteries, J. Inorg.
Organomet. Polym. Mater.
24 (2013) 5–14.
Further examples in: A. Jain, K.A. Persson, G. Ceder. APL Materials (2016).

16. Video tutorials are available
16
www.youtube.com/user/MaterialsProject

17. Outline
17
① Intro to Density Functional Theory (DFT)
② The Materials Project database
③ Next steps

18. DFT methods will become much more powerful
18
types of
materials
high-throughput
screening
computations
predict materials?
relative computing
power
1980s simple metals/
semiconductors
unimaginable by
almost anyone
unimaginable by
majority
1
1990s + oxides unimaginable by
majority
1-2 examples 1000
2000s + complex/
correlated
systems
1-2 examples ~5-10 examples 1,000,000
2010s +hybrid
systems
+excited state
properties?
~many dozens of
examples
~25 examples,
maybe 50 by end
of decade
1,000,000,000*
2020s ?very large
systems?
?routine? ?routine? ?1 trillion?
* The top 2 DOE supercomputers alone have a budget of 8 billion CPU-hours/year, in theory enough to run
basic DFT characterization (structure/charge/band structure) of ~40 million materials/year!

19. Data mining materials properties will be common
• As the quantity of organized materials data (both
simulation and experiment) grows, there will be
increased opportunities to apply statistical
learning / data mining
• New types of “predictive models”: recommender
systems, decision trees, even deep learning
• Some key and upcoming players in the US:
– Citrine Informatics
– IBM Watson
– NIST MGI efforts (ChiMaD, Materials Data Facility)
– U. Buffalo Center for Materials Informatics
– Center for Materials Processing Data
– and our own Materials Project
19
Jain, Hautier, Ong, Persson, New opportunities for materials informatics: Resources and data mining techniques for
uncovering hidden relationships, J. Mater. Res. 31 (2016) 977–994.

20. But remember…
• Accuracy will always be an issue
• Max system size (~1000 atoms today w/o major effort) is another major
limitation
• Not everything can be simulated
– today, you are lucky if you can simulate 20% of what you want to know about a
material for an application with decent accuracy
– translating engineering design criteria into a set of DFT-computable quantities
remains challenging
• Even with many improvements to current technology, this will still just be
a tool in materials discovery and never a complete solution
• But – perhaps we can indeed cut down on materials discovery time by a
factor of two!
20

21. Thank you!
• Dr. Kristin Persson and Prof. Gerbrand Ceder,
founders of Materials Project and their teams
• Prof. Shyue Ping Ong & Prof. Geoffroy Hautier
• NERSC computing center and staff
• Funding: U.S. Department of Energy
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Slides posted to http://www.slideshare.net/anubhavster