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

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?

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What constrains traditional experimentation?

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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)

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

Outline

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①  Intro to Density Functional Theory (DFT)

②  The Materials Project database ③  Next steps

An overview of materials modeling techniques

6 Source: NASA

What is density functional theory (DFT)?

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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.

How does one use DFT to design new materials?

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A. Jain, Y. Shin, and K. A. Persson, Nat. Rev. Mater. 1, 15004 (2016).

How accurate is DFT in practice?

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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).

Outline

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①  Intro to Density Functional Theory (DFT)

②  The Materials Project database

③  Next steps

High-throughput DFT: a key idea

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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)

Examples of (early) high-throughput studies

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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.

Computations predict, experiments confirm

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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).

Another key idea: putting all the data online

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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!

The data is re-used by the community

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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).

Video tutorials are available

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www.youtube.com/user/MaterialsProject

Outline

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①  Intro to Density Functional Theory (DFT)

②  The Materials Project database ③  Next steps

DFT methods will become much more powerful

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types of materials

high-throughput screening

computations predict materials?

relative computing power

1980s simple metals/semiconductors

unimaginable by almost anyone

unimaginable by majority

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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!

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.

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!

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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

21 Slides posted to http://www.slideshare.net/anubhavster