Universal fragment descriptors for predicting properties of inorganic crystals

Abstract

Although historically materials discovery has been driven by a laborious trial-and-error process, knowledge-driven materials design can now be enabled by the rational combination of Machine Learning methods and materials databases. Here, data from the AFLOW repository for ab initio calculations is combined with Quantitative Materials Structure-Property Relationship models to predict important properties: metal/insulator classification, band gap energy, bulk/shear moduli, Debye temperature and heat capacities. The prediction’s accuracy compares well with the quality of the training data for virtually any stoichiometric inorganic crystalline material, reciprocating the available thermomechanical experimental data. The universality of the approach is attributed to the construction of the descriptors: Property-Labelled Materials Fragments. The representations require only minimal structural input allowing straightforward implementations of simple heuristic design rules.

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References 83

1. 1.

   Curtarolo S.. The high-throughput highway to computational materials design. Nat. Mater. 12, 191–201 (2013 ).

   Not in our library10.1038/nmat3568

2. 2.

   Bergerhoff G., Hundt R., Sievers R. & Brown I. D. The inorganic crystal structure data base. J. Chem. Inf. Comput. Sci. 23, 66–69 (1983 ).

   Not in our library

3. 3.

   Belsky A., Hellenbrandt M., Karen V. L. & Luksch P. New developments in the Inorganic Crystal Structure Database (ICSD): accessibility in support of materials research and design. Acta Crystallogr. Sect. B 58, 364–369 (2002 ).

   Not in our library10.1107/s0108768102006948

4. 4.

   Curtarolo S.. AFLOWLIB.ORG: a distributed materials properties repository from high-throughput ab initio calculations. Comput. Mater. Sci. 58, 227–235 (2012 ).

   Not in our library

5. 5.

   Walsh A. Inorganic materials: the quest for new functionality. Nat. Chem. 7, 274–275 (2015 ).

   Not in our library10.1038/nchem.2213

6. 6.

   Castleton C. W. M., Höglund A. & Mirbt S. Managing the supercell approximation for charged defects in semiconductors: Finite-size scaling, charge correction factors, the band-gap problem, and the ab initio dielectric constant. Phys. Rev. B 73, 035215 (2006 ).

   Not in our library

7. 7.

   Lindgren I. in Relativistic Many-Body Theory: A New Field-Theoretical Approach Vol. 63, 199–210Springer (2011 ).

   Not in our library

8. 8.

   van Schilfgaarde M., Kotani T. & Faleev S. Quasiparticle Self-Consistent GW Theory. Phys. Rev. Lett. 96, 226402 (2006 ).

   Not in our library10.1103/physrevlett.96.226402

9. 9.

   Koinuma H. & Takeuchi I. Combinatorial solid-state chemistry of inorganic materials. Nat. Mater. 3, 429–438 (2004 ).

   Not in our library10.1038/nmat1157

10. 10.

    Mizutani U. Hume-Rothery Rules for Structurally Complex Alloy Phases CRC Press (2011 ).

    Not in our library

11. 11.

    Isayev O.. Materials cartography: representing and mining materials space using structural and electronic fingerprints. Chem. Mater. 27, 735–743 (2015 ).

    Not in our library

12. 12.

    Ghiringhelli L. M., Vybiral J., Levchenko S. V., Draxl C. & Scheffler M. Big data of materials science: critical role of the descriptor. Phys. Rev. Lett. 114, 105503 (2015 ).

    Not in our library10.1103/physrevlett.114.105503

13. 13.

    Pyzer-Knapp E. O., Li K. & Aspuru-Guzik A. Learning from the Harvard Clean Energy Project: the use of neural networks to accelerate materials discovery. Adv. Func. Mater 25, 6495–6502 (2015 ).

    Not in our library

14. 14.

    Rajan K. Materials informatics: the materials ‘gene' and big data. Annu. Rev. Mater. Res. 45, 153–169 (2015 ).

    Not in our library

15. 15.

    Carrete J., Mingo N., Wang S. & Curtarolo S. Nanograined half-heusler semiconductors as advanced thermoelectrics: an ab initio high-throughput statistical study. Adv. Func. Mater 24, 7427–7432 (2014 ).

    Not in our library

16. 16.

    van Roekeghem A., Carrete J., Oses C., Curtarolo S. & Mingo N. High-throughput computation of thermal conductivity of high-temperature solid phases: the case of oxide and fluoride perovskites. Phys. Rev. X 6, 041061 (2016 ).

    Not in our library

17. 17.

    Furmanchuk A., Agrawal A. & Choudhary A. Predictive analytics for crystalline materials: bulk modulus. RSC Adv 6, 95246–95251 (2016 ).

    Not in our library

18. 18.

    Duffy J. A. Variable electronegativity of oxygen in binary oxides: possible relevance to molten fluorides. J. Chem. Phys. 67, 2930–2931 (1977 ).

    Not in our library

19. 19.

    Di Quarto F., Sunseri C., Piazza S. & Romano M. C. Semiempirical correlation between optical band gap values of oxides and the difference of electronegativity of the elements. its importance for a quantitative use of photocurrent spectroscopy in corrosion studies. J. Phys. Chem. B 101, 2519–2525 (1997 ).

    Not in our library

20. 20.

    Zeng Y., Chua S. J. & Wu P. On the prediction of ternary semiconductor properties by artificial intelligence methods. Chem. Mater. 14, 2989–2998 (2002 ).

    Not in our library

21. 21.
    Machine learning bandgaps of double perovskites · 2016

    No notice
22. 22.

    Gu T., Lu W., Bao X. & Chen N. Using support vector regression for the prediction of the band gap and melting point of binary and ternary compound semiconductors. Solid State Sci. 8, 129–136 (2006 ).

    Not in our library

23. 23.

    Gorse A.-D. Diversity in medicinal chemistry space. Curr. Top. Med. Chem. 6, 3–18 (2006 ).

    Not in our library10.2174/156802606775193310

24. 24.

    Varnek A., Tropsha A. (eds) Chemoinformatics Approaches to Virtual Screening RSC (2008 ).

    Not in our library

25. 25.

    Toher C.. High-throughput computational screening of thermal conductivity, Debye temperature, and Grüneisen parameter using a quasiharmonic Debye Model. Phys. Rev. B 90, 174107 (2014 ).

    Not in our library

26. 26.

    Toher C.. Combining the AFLOW GIBBS and Elastic Libraries to efficiently and robustly screen thermomechanical properties of solids. Phys. Rev. Mater. (in the press).

    Not in our library

27. 27.

    de Jong M.. A statistical learning framework for materials science: application to elastic moduli of k-nary inorganic polycrystalline compounds. Sci. Rep. 6, 34256 (2016 ).

    Not in our library10.1038/srep34256

28. 28.

    Young S. S., Yuan F. & Zhu M. Chemical descriptors are more important than learning algorithms for modelling. Mol. Informatics 31, 707–710 (2012 ).

    Not in our library10.1002/minf.201200031

29. 29.

    Polishchuk P. G., Kuz'min V. E., Artemenko A. G. & Muratov E. N. Universal approach for structural interpretation of QSAR/QSPR models. Mol. Informatics 32, 843–853 (2013 ).

    Not in our library10.1002/minf.201300029

30. 30.

    Ruggiu F., Marcou G., Varnek A. & Horvath D. ISIDA property-labelled fragment descriptors. Mol. Informatics 29, 855–868 (2010 ).

    Not in our library10.1002/minf.201000099

31. 31.

    Todeschini R. & Consonni V. Handbook of molecular descriptors. Methods and Principles in Medicinal Chemistry Wiley-VCH Verlag GmbH (2000 ).

    Not in our library

32. 32.

    Varnek A., Fourches D., Hoonakker F. & Solov'ev V. P. Substructural fragments: an universal language to encode reactions, molecular and supramolecular structures. J. Comput. Aided Mol. Des. 19, 693–703 (2005 ).

    Not in our library10.1007/s10822-005-9008-0

33. 33.

    Lide D. R. CRC Handbook of Chemistry and Physics 85th edn CRC Press (2004 ).

    Not in our library

34. 34.

    Varnek A.. ISIDA—platform for virtual screening based on fragment and pharmacophoric descriptors. Curr. Comput. Aided Drug Des. 4, 191–198 (2008 ).

    Not in our library

35. 35.

    Blatov V. A. Voronoi-Dirichlet polyhedra in crystal chemistry: theory and applications. Crystallogr. Rev. 10, 249–318 (2004 ).

    Not in our library

36. 36.

    Carlucci L., Ciani G., Proserpio D. M., Mitina T. G. & Blatov V. A. Entangled two-dimensional coordination networks: a general survey. Chem. Rev. 114, 7557–7580 (2014 ).

    Not in our library10.1021/cr500150m

37. 37.

    Zolotarev P. N., Arshad M. N., Asiri A. M., Al-amshany Z. M. & Blatov V. A. A possible route toward expert systems in supramolecular chemistry: 2-periodic h-bond patterns in molecular crystals. Cryst. Growth Des. 14, 1938–1949 (2014 ).

    Not in our library

38. 38.

    Cordero B.. Covalent radii revisited. Dalton Trans. 2008, 2832–2838 (2008 ).

    Not in our library10.1039/b801115j

39. 39.

    Pauling L. The Nature of the Chemical Bond and the Structure of Molecules and Crystals: An Introduction to Modern Structural Chemistry Cornell University Press (1960 ).

    Not in our library

40. 40.

    Parr R. G. & Pearson R. G. Absolute hardness: companion parameter to absolute electronegativity. J. Am. Chem. Soc. 105, 7512–7516 (1983 ).

    Not in our library

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