Combining density functional theory calculations, supercomputing, and data-driven methods to design new materials
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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.
![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)](https://image.slidesharecdn.com/20170327jainspie-170327232014/85/Combining-density-functional-theory-calculations-supercomputing-and-data-driven-methods-to-design-new-materials-3-320.jpg)
Combining density functional theory calculations, supercomputing, and data-driven methods to design new materials
- 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 + )};({ )};({ trH dt trd i i i Ψ= Ψ ∧ ! + H = ∇i 2 i=1 Ne ∑ + Vnuclear (ri) i=1 Ne ∑ + Veffective(ri) i=1 Ne ∑ 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 21 Slides posted to http://www.slideshare.net/anubhavster