A Statistical Learning Framework for Accelerated Bandgap Prediction of Inorganic Compounds

  • Suryanaman ChaubeEmail author
  • Prerna Khullar
  • Sriram Goverapet Srinivasan
  • Beena Rai


This study deals with an application of machine learning (ML) techniques for electronic bandgap predictions of a host of entries in the open-source Materials Project (MP) database and inorganic perovskite compounds. Initially, a dataset of 4616 inorganic compounds having available experimental bandgap data was used to generate predictive ML models—support vector machine, k-nearest neighbors, random forest, kernel ridge regression (KRR), and artificial neural networks—requiring only compositional features based on simple elemental attributes. This was followed by identification of the most crucial features for the bandgap and an evaluation of various performance metrics against the dimensionality of the feature space. The trained KRR model having the highest accuracy was then regressed on more than 22,000 entries in the MP database, and the trends are elucidated. Subsequently, out-of-sample validation was performed on a couple of datasets containing several discovered halide perovskites, in conjunction with ab-initio investigations of the undiscovered ones. Finally, the optimal classification and regression models were employed to classify a dataset of 46,970 unknown inorganic halide perovskites into metals and nonmetals followed by bandgap predictions of the nonmetallic entries.


Machine learning bandgap prediction inorganic compounds Materials Project halide perovskites 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



This research was supported by the TCS-CTO Organization under SWON No. 1009292. The authors also thank Mr. Deepak Jain (Scientist, Physical Sciences Research Area, T.R.D.D.C.) for valuable discussions.

Authors’ contributions

S.C. and P.K. did the machine learning computations, and S.G.S. carried out the DFT computations in this work. S.C. wrote the manuscript. All authors jointly discussed the results and their implications and commented on the manuscript.

Supplementary material

11664_2019_7779_MOESM1_ESM.xlsx (1.9 mb)
Supplementary material 1 (XLSX 1900 kb)
11664_2019_7779_MOESM2_ESM.pdf (331 kb)
Supplementary material 2 (PDF 330 kb)


  1. 1.
    J.C. Snyder, M. Rupp, K. Hansen, K.R. Müller, and K. Burke, Phys. Rev. Lett. 108, 253002 (2012).CrossRefGoogle Scholar
  2. 2.
    G. Montavon, M. Rupp, V. Gobre, A. Vazquez-Mayagoitia, K. Hansen, A. Tkatchenko, K.R. Müller, and O.A. Von Lilienfeld, New J. Phys. 15, 095003 (2013).CrossRefGoogle Scholar
  3. 3.
    V. Botu and R. Ramprasad, Int. J. Quantum Chem. 115, 1074 (2015).CrossRefGoogle Scholar
  4. 4.
    F.A. Faber, L. Hutchison, B. Huang, J. Gilmer, S.S. Schoenholz, G.E. Dahl, O. Vinyals, S. Kearnes, P.F. Riley, and O.A. Von Lilienfeld, J. Chem. Theory Comput. 13, 5255 (2017).CrossRefGoogle Scholar
  5. 5.
    S. Chmiela, A. Tkatchenko, H.E. Sauceda, I. Poltavsky, K.T. Schütt, and K.R. Müller, Sci. Adv. 3, 1603015 (2017).CrossRefGoogle Scholar
  6. 6.
    K. Hayashi, A.M. Pradipto, K. Nozaki, T. Akiyama, T. Ito, T. Oguchi, and K. Nakamura, J. Electron. Mater. 48, 1319 (2019).CrossRefGoogle Scholar
  7. 7.
    Y. He, A.A. Talin, and M.D. Allendorf, ECS J. Solid State Sci. Technol. 6, 236 (2017).CrossRefGoogle Scholar
  8. 8.
    B. Himmetoglu, A. Floris, S. De Gironcoli, and M. Cococcioni, Int. J. Quantum Chem. 114, 14 (2014).CrossRefGoogle Scholar
  9. 9.
    G. Pilania, C. Wang, X. Jiang, S. Rajasekaran, and R. Ramprasad, Sci. Rep. 3, 2810 (2013).CrossRefGoogle Scholar
  10. 10.
    K. Fujimura, A. Seko, Y. Koyama, A. Kuwabara, I. Kishida, K. Shitara, C.A. Fisher, H. Moriwake, and I. Tanaka, Adv. Energy Mater. 3, 980 (2013).CrossRefGoogle Scholar
  11. 11.
    L.M. Ghiringhelli, J. Vybiral, S.V. Levchenko, C. Draxl, and M. Scheffler, Phys. Rev. Lett. 114, 105503 (2015).CrossRefGoogle Scholar
  12. 12.
    T. Gu, W. Lu, X. Bao, and N. Chen, Solid State Sci. 8, 129 (2006).CrossRefGoogle Scholar
  13. 13.
    P. Dey, J. Bible, S. Datta, S. Broderick, J. Jasinski, M. Sunkara, M. Menon, and K. Rajan, Comput. Mater. Sci. 83, 185 (2014).CrossRefGoogle Scholar
  14. 14.
    Y. Zhuo, A. Mansouri Tehrani, and J. Brgoch, J. Phys. Chem. Lett. 9, 1668 (2018).CrossRefGoogle Scholar
  15. 15.
    Felix A. Faber, Luke Hutchison, Bing Huang, Justin Gilmer, Samuel S. Schoenholz, George E. Dahl, Oriol Vinyals, Steven Kearnes, Patrick F. Riley, and O. Anatole Von Lilienfeld, J. Chem. Theory Comput. 13, 5255 (2017).CrossRefGoogle Scholar
  16. 16.
    J.P. Perdew, K. Burke, and M. Ernzerhof, Phys. Rev. Lett. 77, 3865 (1996).CrossRefGoogle Scholar
  17. 17.
    G. Pilania, P.V. Balachandran, C. Kim, and T. Lookman, Front. Mater. 3, 19 (2016).CrossRefGoogle Scholar
  18. 18.
    J. Liang, J. Liu, and Z. Jin, Solar RRL 1, 1700086 (2017).CrossRefGoogle Scholar
  19. 19.
    H. Kim, J.S. Han, S.G. Kim, S.Y. Kim, and H.W. Jang, J. Mater. Chem. C 7, 5226 (2019).CrossRefGoogle Scholar
  20. 20.
    G. Pilania, A. Mannodi-Kanakkithodi, B.P. Uberuaga, R. Ramprasad, J.E. Gubernatis, and T. Lookman, Sci. Rep. 6, 19375 (2016).CrossRefGoogle Scholar
  21. 21.
    F. Ahmed, S. Kumar, N. Arshi, M.S. Anwar, S.N. Heo, and B.H. Koo, Acta Mater. 60, 5190 (2012).CrossRefGoogle Scholar
  22. 22.
    T. Gorishnyy, M. Maldovan, C. Ullal, and E. Thomas, Phys. World 18, 24 (2005).CrossRefGoogle Scholar
  23. 23.
    VanderPlas, J., Python data science handbook: essential tools for working with data, 1st edn. (O’Reilly Media, 2016).Google Scholar
  24. 24.
    P. Thanh Noi and M. Kappas, Sensors 18, 18 (2018).CrossRefGoogle Scholar
  25. 25.
    P.V. Balachandran, D. Xue, J. Theiler, J. Hogden, J.E. Gubernatis, and T. Lookman, Materials Discovery and Design, ed. T. Lookman, S. Eidenbenz, F. Alexander, and C. Barnes (Cham: Springer, 2018), p. 59.CrossRefGoogle Scholar
  26. 26.
    Mishra, S., Sturm, B.L. and Dixon, S., ISMIR, 537 (2017).Google Scholar
  27. 27.
    L.C. Allen, J. Am. Chem. Soc. 111, 9003 (1989).CrossRefGoogle Scholar
  28. 28.
    C.J. Kang, Int. J. Quantum Chem. 118, 25548 (2018).CrossRefGoogle Scholar
  29. 29.
    B.R. Nag, J. Electron. Mater. 26, 70 (1997).CrossRefGoogle Scholar
  30. 30.
    A. Morales-García, R. Valero, and F. Illas, J. Phys. Chem. C 121, 18862 (2017).CrossRefGoogle Scholar
  31. 31.
    M.R. Filip and F. Giustino, Proc. Natl. Acad. Sci. 115, 5397 (2018).CrossRefGoogle Scholar
  32. 32.
    M.R. Filip and F. Giustino, J. Phys. Chem. C 120, 166 (2015).CrossRefGoogle Scholar
  33. 33.
    S. Körbel, M.A. Marques, and S. Botti, J. Mater. Chem. C 4, 3157 (2016).CrossRefGoogle Scholar
  34. 34.
    Z. Xu, Y.V. Joshi, S. Raman, and J.R. Kitchin, J. Chem. Phys. 142, 144701 (2015).CrossRefGoogle Scholar
  35. 35.
    O. Isayev, C. Oses, C. Toher, E. Gossett, S. Curtarolo, and A. Tropsha, Nat. Commun. 8, 15679 (2017).CrossRefGoogle Scholar
  36. 36.
    Y. Li and K. Yang, Energy Environ. Sci. 12, 2233 (2019).CrossRefGoogle Scholar
  37. 37.
    A.M. Leguy, P. Azarhoosh, M.I. Alonso, M. Campoy-Quiles, O.J. Weber, J. Yao, D. Bryant, M.T. Weller, J. Nelson, A. Walsh, M. Van Schilfgaarde, and P.R. Barnes. Nanoscale 8, 6317 (2016).Google Scholar

Copyright information

© The Minerals, Metals & Materials Society 2019

Authors and Affiliations

  1. 1.TCS ResearchTata Research Development and Design CenterPuneIndia

Personalised recommendations