Navigating the combinatorial materials space

Presented at the CECAM workshop on accelerating materials discovery in Liverpool (UK) in July 2019. Various low-cost, high-throughput screening approaches are covered.

Transcript

1. Navigating the combinatorial materials space using chemical heuristics, machine learning and first-principles calculations Dan Davies @danwdavies
2. Overview 1. Combinatorial composition space Bottom-up materials discovery 2. Tiered screening Cheap filters to narrow down the search space 3. Automated first-principles calculations High-throughput DFT
3. Overview 1. Combinatorial composition space
4. Counting materials What proportion of the composition space for materials is represented by known materials?
5. hydrogen 1 H 1.00794 lithium 3 Li 6.941 beryllium 4 Be 9.01218 sodium 11 Na 22.9898 magnesium 12 Mg 24.3050 potassium 19 K 39.0983 calcium 20 Ca 40.078 rubidium 37 Rb 85.4678 strontium 38 Sr 87.62 cesium 55 Cs 132.9055 barium 56 Ba 137.327 scandium 21 Sc 44.9559 titanium 22 Ti 47.867 vanadium 23 V 50.9415 chromium 24 Cr 51.9961 manganese 25 Mn 54.938 iron 26 Fe 55.845 cobalt 27 Co 58.9331 nickel 28 Ni 58.6934 copper 29 Cu 63.546 zinc 30 Zn 65.38 galium 31 Ga 69.723 germanium 32 Ge 72.64 aluminium 13 Al 26.9815 silicon 14 Si 28.0855 boron 5 B 10.811 carbon 6 C 12.0107 nitrogen 7 N 14.0067 oxygen 8 O 15.9994 phosphorus 15 P 30.9737 sulfur 16 S 32.065 arsenic 33 As 74.9216 selenium 34 Se 78.96 fluorine 9 F 18.9984 neon 10 Ne 20.1797 chlorine 17 Cl 35.453 argon 18 Ar 39.948 bromine 35 Br 79.904 krypton 36 Kr 83.798 thallium 81 Tl 204.3833 lead 82 Pb 207.2 indium 49 In 114.818 tin 50 Sn 118.710 antimony 51 Sb 121.760 tellurium 52 Te 127.60 bismuth 83 Bi 208.980 polonium 84 Po 209 iodine 53 I 126.904 xenon 54 Xe 131.293 astatine 85 At 210 radon 86 Rn 222 yttrium 39 Y 88.9059 zirconium 40 Zr 91.224 niobium 41 Nb 92.906 molybdenum 42 Mo 95.96 technetium 43 Tc 98 ruthenium 44 Ru 101.07 rhodium 45 Rh 102.9055 palladium 46 Pd 106.42 silver 47 Ag 107.8682 cadmium 48 Cd 112.411 hafnium 72 Hf 178.49 tantalum 73 Ta 180.9478 tungsten 74 W 183.84 rhenium 75 Re 186.207 osmium 76 Os 190.23 iridium 77 Ir 192.217 platinum 78 Pt 195.084 gold 79 Au 196.9666 mercury 80 Hg 200.59 helium 2 He 4.00260 Walsh Materials Design SMACT Periodic Table lanthanides actinides and other hard-to- pronounce elements +1,-1 +1 +1 +1 +1 +1 +2 +2 +2 +2 +2 +3 +3,+4 tt  +2,+3,+6 +2,+4,+7 +2,+3,+6 +2,+3 +2 +1,+2 +2 +3 tttttt  -3,+3,+5 -2 -1 +3 -4,+4 -3,+3,+5 -2,+2,+4 +6 -1,+1,+3 +5 +7 -1  t  +5,+7 +3 -4,+2,+4 -3,+3,+5 -2,+2,+4 +6 -1,+1,+3 +5 +7 +3 +4 +3,+5 +4,+6 +4,+7 t  +2,+3 +2,+4 +1 +2 +3 -4,+2,+4 -3,+3,+5 -2,+2,+4 +6 +4 +3,+5 t  +4,+6,+7 +4,+8 +3,+4 t  +1,+3 +1,+2 +1,+3 +2,+4 +3,+5 -2,+2,+4 -1,+1 tin 50 Sn 118.710 -4,+2,+4 common oxidation states atomic mass elemental symbol atomic number elemental name +2,+6 +2,+4,+6 +2 403 species of interest The building blocks
6. The combinatorial perspective r Aw Bx Aw Bx Cy Aw Bx Cy Dz nCr 81,003 107 109 With stoichiometry 106 109 1012 n = 403 species Aw Bx Cy Dz w,x,y,z ≤ 8
7. Applying heuristic limits n = 403 species Aw Bx Cy Dz w,x,y,z ≤ 8 1. Charge neutrality: wqA + xqB + yqC + zqD = 0 2. Electronegativity order: Cation < Anion
8. Scale of the composition space “Computational screening of all stoichiometric inorganic materials”, Chem, 2016
9. SMACT Github.com/wmd-group/smact
10. Overview 2. Tiered screening
11. Hierarchy of screening steps LOW COST HIGH COST Sustainability Band gap Electron energies (Hybrid) DFT Structure assignment Compositional filters (Global optimization) Ionic substitution Automated calculations PV absorbers, TCOs, photocatalysts…
12. Band gap screening
13. SSE to screen 160k chalcohalides VASP: HSE06 K-points 0.242 Å-1 cutoff 520 eV “Computer-aided design of metal chalcohalide semiconductors: from chemical composition to crystal structure”, Chem. Sci., 2018 M. Gaultois et al., Chem. Mater., 25, 2911 (2013)
14. SSE accuracy Band gap of 35 ternary semiconductors RMSE = 0.66 eV
15. SSE for other chemistries IP of oxides BaO -4.97 eV SiO2 -9.90 eV Al2 O3 -12.42 eV Range = 4.9 eV SD = 1.44 eV Band gap of 800 oxides
16. Supervised learning x: composition y: GLLB-sc band gap 800 oxides Hackingmaterials.github.io/matminer Castelli et al., Adv. Energy. Mat., 2015, 5, 1400915 ML model New data Output Training data training prediction Predicted band gap 1.1M oxides x: composition
17. Gradient boosting regression Band gap of 800 oxides (10-fold cross validation) RMSE = 0.95 eV + Error + Number of trees “Data-driven discovery of photoactive quaternary oxides using first- principles machine learning”, submitted, available on ChemRxiv
18. Oxide bandgaps Distribution of GLLB-sc band gaps for 800 oxides 8% Distribution of PBE band gap range for oxide compositions “Data-driven discovery of photoactive quaternary oxides using first- principles machine learning”, submitted, available on ChemRxiv
19. Using oxidation state data I. Langmuir, J. Am. Chem. Soc, 41, 868-934, 1919
20. Oxidation state filter “Materials discovery by chemical analogy: role of oxidation states in structure prediction”, Faraday Discussions, 2019 species with anion metal with anion
21. Overview 3. Automated first-principles calculations
22. Hierarchy of screening steps LOW COST HIGH COST Sustainability Band gap Electron energies (Hybrid) DFT Structure assignment Compositional filters (Global optimization) Ionic substitution Automated calculations PV absorbers, TCOs, photocatalysts…
23. Hierarchy of screening steps LOW COST HIGH COST Sustainability Band gap Electron energies (Hybrid) DFT Structure assignment Compositional filters (Global optimization) Ionic substitution Automated calculations PV absorbers, TCOs, photocatalysts…
24. Hierarchy of screening steps LOW COST HIGH COST Sustainability Band gap Electron energies (Hybrid) DFT Structure assignment Compositional filters (Global optimization) Ionic substitution Automated calculations PV absorbers, TCOs, photocatalysts… Known New Relaxed
25. Structure assignment USPEX: Max. two FU/unit cell, 60 structures/generation, 20% random, 50% heredity, 10% permutation, 10% soft mutation, 10% lattice mutation Ionic substitution
26. Stability and properties DFT Thermodynamic stability Kinetic stability Hybrid DFT Band structure Carrier effective mass IP/EA Materialsproject.github.io/fireworks Atomate.org
27. Automated calcs in the cloud
28. Building hybrid workflows Combining: Databases, Statistical models, Heuristic rules, First principles methods ChemRxiv 2019, github.com/WMD-group/Solar_oxides_data
29. Summary • The inorganic composition space – even as defined within strict limits – is vast and hardly explored. • Compositions screening structure DFT can work but requires relevant compositional descriptors. • Simple applications of ML can open new screening pathways.
30. Acknowledgements Aron Walsh Keith Butler Jonathan Skelton Adam Jackson Ben Morgan Artem Oganov Conwei Xie Olexandr Isayev WMD-group.github.io