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![How does traditional materials discovery work?
5
“[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
Levi, Levi, Chasid, Aurbach
J. Electroceramics (2009)](https://image.slidesharecdn.com/jainibm-160520125633/75/Combining-density-functional-theory-calculations-supercomputing-and-data-driven-methods-to-design-new-thermoelectric-materials-5-2048.jpg)
Uploaded byAnubhav Jain
Combining density functional theory calculations, supercomputing, and data-driven methods to design new thermoelectric materials
Combining density functional theory calculations, supercomputing, and data-driven methods to design new thermoelectric materials Anubhav Jain presents on using computational methods like density functional theory calculations combined with large datasets and machine learning to design new thermoelectric materials. He discusses how DFT can be used for high-throughput screening of many materials to discover promising candidates. He highlights the Materials Project database which has calculated properties of over 65,000 materials and is used by many researchers. An example is given of screening over 50,000 compounds to find new thermoelectric materials like TmAgTe2 which was later experimentally verified. The goal is to accelerate materials discovery through these computational approaches.
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Combining density functional theory calculations, supercomputing, and data-driven methods to design new thermoelectric materials
- 1. Combining density functionaltheory calculations, supercomputing, and data-driven methods to design new thermoelectric materials Anubhav Jain Energy Technologies Area Lawrence Berkeley National Laboratory Berkeley, CA Slides posted to http://www.slideshare.net/anubhavster
- 2. Making renewable energya reality 2 cost/effort to implement+deploy new technology cost/benefit to maintain new technology cost/benefit to end user of today’s technology) STAGE 1 STAGE 2 STAGE 3 carbon capture/storage energy efficiency retrofits electric vehicles today SolarCity solar panels hybrid electric vehicles Role of Energy Technologies Area at LBNL
- 3. How to movetechnologies across stages? 3 resource constraints over time policy / carbon tax reduce labor/installation cost policy / incentives / rebates new business models (“leasing”) better manufacturing performance engineering new inventions materials optimization materials discovery areas that I work on ETA has a broad portfolio that encompasses a mix of strategies
- 4. Better materials arean important but difficult route • 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/LCO (basis of today’s Li battery electrodes) since 1990 • Why is discovering better materials such a challenge? 4
- 5. How does traditionalmaterials discovery work? 5 “[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 Levi, Levi, Chasid, Aurbach J. Electroceramics (2009)
- 6. Can we inventother, 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 6 www.whitehouse.gov/mgi
- 7. Outline 7 ① Intro toDensity Functional Theory (DFT) ② The Materials Project database ③ Searching for thermoelectric materials ④ Future of Materials Design
- 8. An overview ofmaterials modeling techniques 8 Source: NASA
- 9. What is densityfunctional theory (DFT)? 9 + )};({ )};({ 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 many factors, including 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.
- 10. How does oneuse DFT to design new materials? 10 A. Jain, Y. Shin, and K. A. Persson, Nat. Rev. Mater. 1, 15004 (2016).
- 11. How accurate isDFT in practice? 11 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).
- 12. Viewpoint of theDFT accuracy situation • More accurate would certainly be better – Many researchers are working on this problem, including MSD at LBNL and UC Berkeley – New and better methods do appear over time, e.g., hybrid functionals for solids. • But – let’s not wait for perfection before we start applying it. 12 Time to set sail and leave port!
- 13. Outline 13 ① Intro toDensity Functional Theory (DFT) ② The Materials Project database ③ Searching for thermoelectric materials ④ Future of Materials Design
- 14. High-throughput DFT: akey idea 14 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)
- 15. Examples of (early)high-throughput studies 15 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.
- 16. Computations predict, experimentsconfirm 16 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).
- 17. Another key idea:putting all the data online 17 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 >17,000 registered users around the world >65,000 compounds calculated Data includes • thermodynamic props. • electronic band structure • aqueous stability (E-pH) • elasticity tensors >75 million CPU-hours invested = massive scale!
- 18. The data isre-used by the community 18 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).
- 19. Video tutorials areavailable 19 www.youtube.com/user/MaterialsProject
- 20. A peek intothe future? 20
- 21. Outline 21 ① Intro toDensity Functional Theory (DFT) ② The Materials Project database ③ Searching for thermoelectric materials ④ Future of Materials Design
- 22. Thermoelectric materials • Athermoelectric material generates a voltage based on applied thermal gradient – picture a charged gas that diffuses from hot to cold until the electric field balances the thermal gradient • The voltage per Kelvin is the Seebeck coefficient • A thermoelectric module improves voltage and power by linking together n and p type materials 22 www.alphabetenergy.com
- 23. Why are thermoelectricsuseful? 23 • Applications: energy from heat, refrigeration • Already used in spacecraft and high-end car seat coolers • Large-scale waste heat recovery is targeted Alphabet Energy – 25kW generator
- 24. Thermoelectric figure ofmerit 24 • Require new, abundant materials that possess a high “figure of merit”, or zT, for high efficiency • Target: zT at least 1, ideally >2 ZT = α2σT/κ power factor >2 mW/mK2 (PbTe=10 mW/mK2) Seebeck coefficient > 100 V/K Band structure + Boltztrap electrical conductivity > 103 /(ohm-cm) Band structure + Boltztrap thermal conductivity < 1 W/(m*K) • e from Boltztrap • l difficult (phonon-phonon scatterin
- 25. How zT relatesto power generation efficiency 25 C. B. Vining, Nat. Mater. 8, 83 (2009).
- 26. Thermoelectric materials areimproving over time 26 Also, many new materials have been recently discovered around the zT=1 range, e.g. tetrahedrites SnSe zT=2.62 reported in 2014 J. P. Heremans, M. S. Dresselhaus, L. E. Bell, and D. T. Morelli, Nat. Nanotechnol. 8, 471 (2013). G. J. Snyder and E. S. Toberer, 7, 105 (2008).
- 27. We’ve initiated asearch for thermoelectric materials 27 Initial procedure similar to Madsen (2006) On top of this traditional procedure we add: • thermal conductivity model of Pohl-Cahill • targeted defect calculations to assess doping • Today - ~50,000 compounds screened! Madsen, G. K. H. Automated search for new thermoelectric materials: the case of LiZnSb. J. Am. Chem. Soc., 2006, 128, 12140–6
- 28. New Materials fromscreening – TmAgTe2 (calcs) 28 Zhu, H.; Hautier, G.; Aydemir, U.; Gibbs, Z. M.; Li, G.; Bajaj, S.; Pöhls, J.-H.; Broberg, D.; Chen, W.; Jain, A.; White, M. A.; Asta, M.; Snyder, G. J.; Persson, K.; Ceder, G. Computational and experimental investigation of TmAgTe 2 and XYZ 2 compounds, a new group of thermoelectric materials identified by first-principles high-throughput screening, J. Mater. Chem. C, 2015, 3
- 29. TmAgTe2 - experiments 29 Zhu,H.; Hautier, G.; Aydemir, U.; Gibbs, Z. M.; Li, G.; Bajaj, S.; Pöhls, J.-H.; Broberg, D.; Chen, W.; Jain, A.; White, M. A.; Asta, M.; Snyder, G. J.; Persson, K.; Ceder, G. Computational and experimental investigation of TmAgTe 2 and XYZ 2 compounds, a new group of thermoelectric materials identified by first-principles high-throughput screening, J. Mater. Chem. C, 2015, 3
- 30. The limitation -doping 30 p=1020 VB Edge CB Edge n=1020 1016 E-Ef (eV) TmAgTe2 600K Our Sample 2 1 3 4 1 2 4 3 Te Te Tm AgY AgTmAg TmAg2 YAg TmTe TmAgTe2 Ag2Te YTe YAgTe2 Ag2Te Y6AgTe2 Region 1 Region 2 Region 3 Region 4 • Calculations indicate TmAg defects are most likely “hole killers”. • Tm deficient samples so far not successful • Meanwhile, explore other chemistries
- 31. YCuTe2 – friendlierelements, higher zT (0.75) 31 • A combination of intuition and calculations suggest to try YCuTe2 • Higher carrier concentration of ~1019 • Retains very low thermal conductivity, peak zT ~0.75 • But – unlikely to improve further Aydemir, U.; Pöhls, J.-H.; Zhu, H.l Hautier, G.; Bajaj, S.; Gibbs, Z. M.; Chen, W.; Li, G.; Broberg, D.; Kang, S.D.; White, M. A.; Asta, M.; Ceder, G.; Persson, K.; Jain, A.; Snyder, G. J. YCuTe2: A Member of a New Class of Thermoelectric Materials with CuTe4- Based Layered Structure. J. Mat Chem C, 2016 experiment computation
- 32. Outline 32 ① Intro toDensity Functional Theory (DFT) ② The Materials Project database ③ Searching for thermoelectric materials ④ Future of Materials Design
- 33. DFT methods willbecome much more powerful 33 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 ?linear scaling? ?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!
- 34. We will relymore on computers to optimize materials 34 During World War II, no team of human cryptographers could decode the German Enigma machine. Alan Turing succeeded where others failed for two reasons: 1. He built a very large computing machine that could test whether a given parameter combination represented a good solution 2. When brute force was not enough, he devised clever statistical tests to greatly narrow down the possibilities to assist the computer A similar system might be useful for materials optimization.
- 35.
- 36. http://xkcd.com/1002/ NASA antenna design http://en.wikipedia.org/wiki/Evolved_antenna this antenna isthe product of a radiation model+genetic algorithm solver. It was better than human designs and launched into space.
- 37. But remember… • Accuracywill always be an issue • 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 • 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! 37
- 38. Thank you! • Dr.Kristin Persson and Prof. Gerbrand Ceder, founders of Materials Project and their teams • Prof. Shyue Ping Ong (pymatgen) • Prof. Geoffroy Hautier (thermoelectrics) • Prof. Jeffrey Snyder + team (thermoelectrics) • Prof. Mary Anne White + team (thermoelectrics) • Prof. Mark Asta and team (thermoelectrics) • Prof. Karsten Jacobsen + team (perovskite GA) • NERSC computing center and staff • Funding: U.S. Department of Energy 38 Slides posted to http://www.slideshare.net/anubhavster