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Virtual high-throughput screening of photoactive quaternary oxides

963f83cdd6c15fdba1fa247eaf448940?s=47 Dan Davies
February 28, 2021

Virtual high-throughput screening of photoactive quaternary oxides

MRS Fall Meeting 2019, MT03.

963f83cdd6c15fdba1fa247eaf448940?s=128

Dan Davies

February 28, 2021
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  1. Virtual high-throughput screening of photoactive quaternary oxides Dan Davies MRS

    Fall Meeting MT03 5th December 2019 @danwdavies
  2. Overview LOW COST HIGH COST Sustainability Band gap Electron energies

    (Hybrid) DFT Structure assignment Compositional filters Global optimization Data-driven approach Automated calculations PV absorbers, TCOs, photocatalysts…
  3. Overview 1. Combinatorial composition space

  4. 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 +2 103 elements 403 species The building blocks
  5. 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: 𝝌 Cation < 𝝌 Anion
  6. Scale of the composition space “Computational screening of all stoichiometric

    inorganic materials”, Chem, 2016
  7. SMACT Github.com/wmd-group/smact

  8. Overview 2. Tiered screening

  9. Overview LOW COST HIGH COST Sustainability Band gap Electron energies

    (Hybrid) DFT Structure assignment Compositional filters Global optimization Data-driven approach Automated calculations PV absorbers, TCOs, photocatalysts…
  10. Band gap screening

  11. 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)
  12. SSE accuracy Band gap of 35 ternary semiconductors RMSE =

    0.66 eV
  13. 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
  14. 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
  15. 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”, Chem. Mater., 2019
  16. 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”, Chem. Mater., 2019
  17. Overview LOW COST HIGH COST Sustainability Band gap Electron energies

    (Hybrid) DFT Structure assignment Compositional filters Global optimization Data-driven approach Automated calculations PV absorbers, TCOs, photocatalysts…
  18. Stability and properties DFT Thermodynamic stability Kinetic stability Hybrid DFT

    Band structure Carrier effective mass IP/EA Materialsproject.github.io/fireworks Atomate.org
  19. Stability and properties MnAg(SeO3 )2 Materialsproject.github.io/fireworks Atomate.org

  20. Building hybrid workflows Combining: • Databases • Statistical models •

    Heuristic rules • First-principles methods github.com/WMD-group/Solar_oxides_data
  21. 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 enable new screening pathways.
  22. Data access github.com/WMD-group/Solar_oxides_data

  23. Acknowledgements Aron Walsh Keith Butler Jonathan Skelton Adam Jackson Ben

    Morgan Olexandr Isayev WMD-group.github.io
  24. Extra: Sustainability metrics M. Gaultois et al., Chem. Mater., 25,

    2911 (2013)
  25. Extra: ML for materials science Targeting discovery of new compounds

    Enhancing theoretical chemistry Assisting characterization Mining existing literature K. T. Butler, D. W. Davies, H. Cartwright, O. Isayev, A. Walsh, Machine learning for molecular and materials science, Nature, 2018
  26. Extra: Gradient boosting regression An ensemble method that uses many

    weak learners, e.g. decision trees Node Leaf Tree depth At each node, the space is split such that samples with similar labels are grouped together
  27. Extra: Gradient boosting regression Boosting: Incrementally add trees to minimize

    a loss function + + … Root mean squared error (RMSE) Next decision tree Learning rate An ensemble method that uses many weak learners, e.g. decision trees