Skip to main content
SpringerLink
Log in

Adaptive machine learning for efficient materials design

  • Technical Feature
  • Published:
MRS Bulletin Aims and scope Submit manuscript

Abstract

Applying machine learning (ML) methods to accelerate the search for new materials with improved properties has gained increasing attention in recent years. Using nonadaptive ML approaches that do not have an iterative feedback loop can perform poorly in extrapolations at previously unexplored search space, especially when trained on small data sets. We performed numerical simulations on two data sets that exhibit distinct composition-property relationships and explored the relative efficacies of adaptive ML strategies in identifying the optimal material composition with the highest. Adaptive ML methods show promise for extrapolation and find compositions with properties better than those in the training data, but the rate of discovery is dictated by the nuances of the composition-property landscape. The outcome of this work has key implications in developing strategies that employ ML methods for navigating a vast search space of combinatorial possibilities.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5

Similar content being viewed by others

References

  1. J.A. Haber, Y. Cai, S.H. Jung, C.X. Xiang, S. Mitrovic, J. Jin, A.T. Bell, J.M. Gregoire, Energy Environ. Sci. 7 (2), 682 (2014).

    Google Scholar 

  2. R. Potyrailo, K. Rajan, K. Stoewe, I. Takeuchi, B. Chisholm, H. Lam, ACS Comb. Sci. 13 (6), 579 (2011).

    Google Scholar 

  3. I. Takeuchi, R.B. van Dover, H. Koinuma, MRS Bull. 27 (4), 301 (2002).

    Google Scholar 

  4. X.D. Xiang, Annu. Rev. Mater. Sci. 29, 149 (1999).

    Google Scholar 

  5. Q.M. Yan, J. Yu, S.K. Suram, L. Zhou, A. Shinde, P.F. Newhouse, W. Chen, G. Li, K.A. Persson, J.M. Gregoire, J.B. Neaton, Proc. Natl. Acad. Sci. U.S.A. 114 (12), 3040 (2017).

    Google Scholar 

  6. J. Cui, Y.S. Chu, O.O. Famodu, Y. Furuya, J. Hattrick-Simpers, R.D. James, A. Ludwig, S. Thienhaus, M. Wuttig, Z.Y. Zhang, I. Takeuchi, Nat. Mater. 5 (4), 286 (2006).

    Google Scholar 

  7. P. Hohenberg, W. Kohn, Phys. Rev. B 136 (3b), B864 (1964).

    Google Scholar 

  8. W. Kohn, L.J. Sham, Phys. Rev. 140 (4a), 1133 (1965).

    Google Scholar 

  9. S. Nose, Mol. Phys. 52 (2), 255 (1985).

    Google Scholar 

  10. T. Burkert, L. Nordstrom, O. Eriksson, O. Heinonen, Phys. Rev. Lett. 93 (2) (2004).

    Google Scholar 

  11. J. Hafner, C. Wolverton, G. Ceder, MRS Bull. 31 (9), 659 (2006).

    Google Scholar 

  12. H. Takenaka, I. Grinberg, S. Liu, A.M. Rappe, Nature 546 (7658), 391 (2017).

    Google Scholar 

  13. G. Andersson, T. Burkert, P. Warnicke, M. Björck, B. Sanyal, C. Chacon, C. Zlotea, L. Nordström, P. Nordblad, O. Eriksson, Phys. Rev. Lett. 96, 037205 (2006).

    Google Scholar 

  14. J.J.P. Peters, A.M. Sanchez, D. Walker, R. Whatmore, R. Beanland, Adv. Mater. 31, 1806498 (2019).

    Google Scholar 

  15. S. Curtarolo, G.L.W. Hart, M.B. Nardelli, N. Mingo, S. Sanvito, O. Levy, Nat. Mater. 12 (3), 191 (2013).

    Google Scholar 

  16. S. Curtarolo, W. Setyawan, S.D. Wang, J.K. Xue, K.S. Yang, R.H. Taylor, L.J. Nelson, G.L.W. Hart, S. Sanvito, M. Buongiorno-Nardelli, N. Mingo, O. Levy, Comput. Mater. Sci. 58, 227 (2012).

    Google Scholar 

  17. A. Jain, S.P. Ong, G. Hautier, W. Chen, W.D. Richards, S. Dacek, S. Cholia, D. Gunter, D. Skinner, G. Ceder, K.A. Persson, APL Mater. 1 (1), 011002 (2013).

    Google Scholar 

  18. J.E. Saal, S. Kirklin, M. Aykol, B. Meredig, C. Wolverton, JOM 65 (11), 1501 (2013).

    Google Scholar 

  19. A. Agrawal, A. Choudhary, APL Mater. 4 (5) (2016).

    Google Scholar 

  20. P.V. Balachandran, S.R. Broderick, K. Rajan, Proc. R. Soc. Lond. A 467 (2132), 2271 (2011).

    Google Scholar 

  21. P.V. Balachandran, A.A. Emery, J.E. Gubernatis, T. Lookman, C. Wolverton, A. Zunger, Phys. Rev. Mater. 2 (4), 043802 (2018).

    Google Scholar 

  22. P.V. Balachandran, B. Kowalski, A. Sehirlioglu, T. Lookman, Nat. Commun. 9 (1), 1668 (2018).

    Google Scholar 

  23. P.V. Balachandran, J. Theiler, J.M. Rondinelli, T. Lookman, Sci. Rep. 5, 13285 (2015).

    Google Scholar 

  24. P.V. Balachandran, D. Xue, J. Theiler, J. Hogden, T. Lookman, Sci. Rep. 6, 19660 (2016).

    Google Scholar 

  25. P.V. Balachandran, D.Z. Xue, T. Lookman, Phys. Rev. B 93 (14), 144111 (2016).

    Google Scholar 

  26. P.V. Balachandran, J. Young, T. Lookman, J.M. Rondinelli, Nat. Commun. 8, 14282 (2017).

    Google Scholar 

  27. L.M. Ghiringhelli, J. Vybiral, S.V. Levchenko, C. Draxl, M. Scheffler, Phys. Rev. Lett. 114 (10), 105503 (2015).

    Google Scholar 

  28. G.X. Gu, C.T. Chen, M.J. Buehler, Extreme Mech. Lett. 18, 19 (2018).

    Google Scholar 

  29. G.X. Gu, C.T. Chen, D.J. Richmond, M.J. Buehler, Mater. Horiz. 5 (5), 939 (2018).

    Google Scholar 

  30. H.C. Herbol, W.C. Hu, P. Frazier, P. Clancy, M. Poloczek, npj Comput. Mater. 4 (1), 51 (2018).

    Google Scholar 

  31. O. Isayev, D. Fourches, E.N. Muratov, C. Oses, K. Rasch, A. Tropsha, S. Curtarolo, Chem. Mater. 27 (3), 735 (2015).

  32. F. Legrain, J. Carrete, A. van Roekeghem, G.K.H. Madsen, N. Mingo, J. Phys. Chem. B 122 (2), 625 (2018).

    Google Scholar 

  33. T. Lookman, P.V. Balachandran, D.Z. Xue, R.H. Yuan, npj Comput. Mater. 5, 21 (2019).

    Google Scholar 

  34. A. Mannodi-Kanakkithodi, G.M. Treich, T.D. Huan, R. Ma, M. Tefferi, Y. Cao, G.A. Sotzing, R. Ramprasad, Adv. Mater. 28 (30), 6277 (2016).

    Google Scholar 

  35. B. Meredig, A. Agrawal, S. Kirklin, J.E. Saal, J.W. Doak, A. Thompson, K. Zhang, A. Choudhary, C. Wolverton, Phys. Rev. B 89 (9), 094104 (2014).

    Google Scholar 

  36. G. Pilania, C.C. Wang, X. Jiang, S. Rajasekaran, R. Ramprasad, Sci. Rep. 3, 2810 (2013).

    Google Scholar 

  37. K. Rajan, Annu. Rev. Mater. Res. 45, 153 (2015).

    Google Scholar 

  38. F. Ren, L. Ward, T. Williams, K.J. Laws, C. Wolverton, J. Hattrick-Simpers, A. Mehta, Sci. Adv. 4, 4 (2018).

    Google Scholar 

  39. A. Seko, A. Togo, H. Hayashi, K. Tsuda, L. Chaput, I. Tanaka, Phys. Rev. Lett. 115 (20), 205901 (2015).

    Google Scholar 

  40. T. Ueno, T.D. Rhone, Z. Hou, T. Mizoguchi, K. Tsuda, Mater. Discov. 4, 18 (2016).

    Google Scholar 

  41. D. Xue, P.V. Balachandran, J. Hogden, J. Theiler, D. Xue, T. Lookman, Nat. Commun. 7, 11241 (2016).

    Google Scholar 

  42. D. Xue, P.V. Balachandran, R. Yuan, T. Hu, X. Qian, E.R. Dougherty, T. Lookman, Proc. Nat. Acad. Sci. U.S.A. 113 (47), 13301 (2016).

    Google Scholar 

  43. M. Yamawaki, M. Ohnishi, S.H. Ju, J. Shiomi, Sci. Adv. 4, 6 (2018).

    Google Scholar 

  44. R. Yuan, Z. Liu, P.V. Balachandran, D. Xue, Y. Zhou, X. Ding, J. Sun, D. Xue, T. Lookman, Adv. Mater. 30, 7 (2018).

    Google Scholar 

  45. Y. Zhuo, A.M. Tehrani, A.O. Oliynyk, A.C. Duke, J. Brgoch, Nat. Commun. 9 (1), 4377 (2018).

    Google Scholar 

  46. P.V. Balachandran, D. Xue, J. Theiler, J. Hogden, J.E. Gubernatis, T. Lookman, in Materials Discovery and Design, T. Lookman, S. Eidenbenz, F. Alexander, C Barnes, Eds. (Springer, Berlin, Germany, 2018), vol. 280.

  47. C. Kim, G. Pilania, R. Ramprasad, Chem. Mater. 28 (5), 1304 (2016).

    Google Scholar 

  48. C.W. Myung, J. Yun, G. Lee, K.S. Kim, Adv. Energy Mater. 8, 14 (2018).

    Google Scholar 

  49. C.W. Rosenbrock, E.R. Homer, G. Csanyi, G.L.W. Hart, npj Comput. Mater. 3, 747 (2017).

    Google Scholar 

  50. R. Woods-Robinson, D. Broberg, A. Faghaninia, A. Jain, S.S. Dwaraknath, K.A. Persson, Chem. Mater. 30 (22), 8375 (2018).

    Google Scholar 

  51. R. Dehghannasiri, D.Z. Xue, P.V. Balachandran, M.R. Yousefi, L.A. Dalton, T. Lookman, E.R. Dougherty, Comput. Mater. Sci. 129, 311 (2017).

    Google Scholar 

  52. D.R. Jones, M. Schonlau, W.J. Welch, J. Glob. Optim. 13 (4), 455 (1998).

    Google Scholar 

  53. T. Lookman, P.V. Balachandran, D.Z. Xue, J. Hogden, J. Theiler, Curr. Opin. Solid State Mater. Sci. 21 (3), 121 (2017).

    Google Scholar 

  54. Y.F. Wang, K.G. Reyes, K.A. Brown, C.A. Mirkin, W.B. Powell, SIAM J. Sci. Comput. 37 (3), B361 (2015).

    Google Scholar 

  55. H.K.D.H. Bhadeshia, ISIJ Int. 39 (10), 966 (1999).

    Google Scholar 

  56. A. Seko, T. Maekawa, K. Tsuda, I. Tanaka, Phys. Rev. B 89 (5), 054303 (2014).

    Google Scholar 

  57. L. Breiman, Mach. Learn. 45 (1), 5 (2001).

    Google Scholar 

  58. J. Ling, M. Hutchinson, E. Antono, S. Paradiso, B. Meredig, Integr. Mater. Manuf. Innov. 6 (3), 207 (2017).

    Google Scholar 

  59. L. Bassman, P. Rajak, R.K. Kalia, A. Nakano, F. Sha, J.F. Sun, D.J. Singh, M. Aykol, P. Huck, K. Persson, P. Vashishta, npj Comput. Mater. 4, 74 (2018).

    Google Scholar 

  60. E.V. Podryabinkin, A.V. Shapeev, Comput. Mater. Sci. 140, 171 (2017).

    Google Scholar 

  61. D. Xue, R. Yuan, Y. Zhou, P.V. Balachandran, X. Ding, J. Sun, T. Lookman, Acta Mater. 125, 532 (2017).

    Google Scholar 

  62. J. Balfer, J. Bajorath, PLoS One 10 (3), e0119301 (2015).

    Google Scholar 

  63. B. Meredig, E. Antono, C. Church, M. Hutchinson, J.L. Ling, S. Paradiso, B. Blaiszik, I. Foster, B. Gibbons, J. Hattrick-Simpers, A. Mehta, L. Ward, Mol. Syst. Des. Eng. 3 (5), 819 (2018).

    Google Scholar 

  64. M. Acosta, N. Novak, V. Rojas, S. Patel, R. Vaish, J. Koruza, G.A. Rossetti, J. Rodel, Appl. Phys. Rev. 4 (4), 041305 (2017).

    Google Scholar 

  65. D. Damjanovic, Rep. Prog. Phys. 61 (9), 1267 (1998).

    Google Scholar 

  66. P.V. Balachandran, T. Shearman, J. Theiler, T. Lookman, Acta Crystallogr. 73 (Pt. 5), 962 (2017).

    Google Scholar 

  67. I. Grinberg, M.R. Suchomel, P.K. Davies, A.M. Rappe, J. Appl. Phys. 98 (9), 094111 (2005).

    Google Scholar 

  68. B. Efron, “Bootstrap Methods: Another Look at the Jackknife,” in Breakthroughs in Statistics: Methodology and Distribution, S. Kotz, N.L. Johnson, Eds. (Springer, New York, NY, 1992), pp. 569–593.

    Google Scholar 

  69. A.J. Smola, B.A. Scholkopf, Stat. Comput. 14 (3), 199 (2004).

    Google Scholar 

  70. R. Guha, J.H. Van Drie, J. Chem. Inf. Model. 48 (3), 646 (2008).

    Google Scholar 

  71. D. Stumpfe, J. Bajorath, J. Med. Chem. 55 (7), 2932 (2012).

    Google Scholar 

  72. J. Theiler, B.G. Zimmer, Stat. Anal. Data Min. 10 (4), 211 (2017).

    Google Scholar 

Download references

Acknowledgments

Thank you to Turab Lookman for topical and intellectual discussions. Support from the Advanced Research Computing Services at the University of Virginia for performing the numerical simulations in the Rivanna cluster is also acknowledged.

Author information

Authors and Affiliations

  1. University of Virginia, USA

    Prasanna V. Balachandran

Authors
  1. Prasanna V. Balachandran
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Prasanna V. Balachandran.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Balachandran, P.V. Adaptive machine learning for efficient materials design. MRS Bulletin 45, 579–586 (2020). https://doi.org/10.1557/mrs.2020.163

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1557/mrs.2020.163

Search

Navigation