Hostname: page-component-586b7cd67f-dsjbd Total loading time: 0 Render date: 2024-11-27T21:34:27.003Z Has data issue: false hasContentIssue false
  • English
  • Français

Adaptive machine learning for efficient materials design

Published online by Cambridge University Press:  13 July 2020

Prasanna V. Balachandran
Prasanna V. Balachandran*
Affiliation:
University of Virginia, USA; [email protected]
Get access

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.

Type
Technical Feature
Information
MRS Bulletin , Volume 45 , Issue 7: Nanomaterials for Electrochemical Water Splitting , July 2020 , pp. 579 - 586
Check for updates
Copyright
Copyright © 2020 Materials Research Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Haber, J.A., Cai, Y., Jung, S.H., Xiang, C.X., Mitrovic, S., Jin, J., Bell, A.T., Gregoire, J.M., Energy Environ. Sci. 7 (2), 682 (2014).CrossRefGoogle Scholar
Potyrailo, R., Rajan, K., Stoewe, K., Takeuchi, I., Chisholm, B., Lam, H., ACS Comb. Sci. 13 (6), 579 (2011).CrossRefGoogle Scholar
Takeuchi, I., van Dover, R.B., Koinuma, H., MRS Bull. 27 (4), 301 (2002).Google Scholar
Xiang, X.D., Annu. Rev. Mater. Sci. 29, 149 (1999).CrossRefGoogle Scholar
Yan, Q.M., Yu, J., Suram, S.K., Zhou, L., Shinde, A., Newhouse, P.F., Chen, W., Li, G., Persson, K.A., Gregoire, J.M., Neaton, J.B., Proc. Natl. Acad. Sci. U.S.A. 114 (12), 3040 (2017).Google Scholar
Cui, J., Chu, Y.S., Famodu, O.O., Furuya, Y., Hattrick-Simpers, J., James, R.D., Ludwig, A., Thienhaus, S., Wuttig, M., Zhang, Z.Y., Takeuchi, I., Nat. Mater. 5 (4), 286 (2006).CrossRefGoogle Scholar
Hohenberg, P., Kohn, W., Phys. Rev. B 136 (3b), B864 (1964).CrossRefGoogle Scholar
Kohn, W., Sham, L.J., Phys. Rev. 140 (4a), 1133 (1965).CrossRefGoogle Scholar
Nose, S., Mol. Phys. 52 (2), 255 (1985).Google Scholar
Burkert, T., Nordstrom, L., Eriksson, O., Heinonen, O., Phys. Rev. Lett. 93 (2) (2004).CrossRefGoogle Scholar
Hafner, J., Wolverton, C., Ceder, G., MRS Bull. 31 (9), 659 (2006).CrossRefGoogle Scholar
Takenaka, H., Grinberg, I., Liu, S., Rappe, A.M., Nature 546 (7658), 391 (2017).CrossRefGoogle Scholar
Andersson, G., Burkert, T., Warnicke, P., Björck, M., Sanyal, B., Chacon, C., Zlotea, C., Nordström, L., Nordblad, P., Eriksson, O., Phys. Rev. Lett. 96, 037205 (2006).Google Scholar
Peters, J.J.P., Sanchez, A.M., Walker, D., Whatmore, R., Beanland, R., Adv. Mater. 31, 1806498 (2019).CrossRefGoogle Scholar
Curtarolo, S., Hart, G.L.W., Nardelli, M.B., Mingo, N., Sanvito, S., Levy, O., Nat. Mater. 12 (3), 191 (2013).CrossRefGoogle Scholar
Curtarolo, S., Setyawan, W., Wang, S.D., Xue, J.K., Yang, K.S., Taylor, R.H., Nelson, L.J., Hart, G.L.W., Sanvito, S., Buongiorno-Nardelli, M., Mingo, N., Levy, O., Comput. Mater. Sci. 58, 227 (2012).Google Scholar
Jain, A., Ong, S.P., Hautier, G., Chen, W., Richards, W.D., Dacek, S., Cholia, S., Gunter, D., Skinner, D., Ceder, G., Persson, K.A., APL Mater. 1 (1), 011002 (2013).CrossRefGoogle Scholar
Saal, J.E., Kirklin, S., Aykol, M., Meredig, B., Wolverton, C., JOM 65 (11), 1501 (2013).CrossRefGoogle Scholar
Agrawal, A., Choudhary, A., APL Mater. 4 (5) (2016).CrossRefGoogle Scholar
Balachandran, P.V., Broderick, S.R., Rajan, K., Proc. R. Soc. Lond. A 467 (2132), 2271 (2011).CrossRefGoogle Scholar
Balachandran, P.V., Emery, A.A., Gubernatis, J.E., Lookman, T., Wolverton, C., Zunger, A., Phys. Rev. Mater. 2 (4), 043802 (2018).Google Scholar
Balachandran, P.V., Kowalski, B., Sehirlioglu, A., Lookman, T., Nat. Commun. 9 (1), 1668 (2018).CrossRefGoogle Scholar
Balachandran, P.V., Theiler, J., Rondinelli, J.M., Lookman, T., Sci. Rep. 5, 13285 (2015).CrossRefGoogle Scholar
Balachandran, P.V., Xue, D., Theiler, J., Hogden, J., Lookman, T., Sci. Rep. 6, 19660 (2016).CrossRefGoogle Scholar
Balachandran, P.V., Xue, D.Z., Lookman, T., Phys. Rev. B 93 (14), 144111 (2016).CrossRefGoogle Scholar
Balachandran, P.V., Young, J., Lookman, T., Rondinelli, J.M., Nat. Commun. 8, 14282 (2017).CrossRefGoogle Scholar
Ghiringhelli, L.M., Vybiral, J., Levchenko, S.V., Draxl, C., Scheffler, M., Phys. Rev. Lett. 114 (10), 105503 (2015).CrossRefGoogle Scholar
Gu, G.X., Chen, C.T., Buehler, M.J., Extreme Mech. Lett. 18, 19 (2018).CrossRefGoogle Scholar
Gu, G.X., Chen, C.T., Richmond, D.J., Buehler, M.J., Mater. Horiz. 5 (5), 939 (2018).CrossRefGoogle Scholar
Herbol, H.C., Hu, W.C., Frazier, P., Clancy, P., Poloczek, M., npj Comput. Mater. 4 (1), 51 (2018).CrossRefGoogle Scholar
Isayev, O., Fourches, D., Muratov, E.N., Oses, C., Rasch, K., Tropsha, A., Curtarolo, S., Chem. Mater. 27 (3), 735 (2015).CrossRefGoogle Scholar
Legrain, F., Carrete, J., van Roekeghem, A., Madsen, G.K.H., Mingo, N., J. Phys. Chem. B 122 (2), 625 (2018).CrossRefGoogle Scholar
Lookman, T., Balachandran, P.V., Xue, D.Z., Yuan, R.H., npj Comput. Mater. 5, 21 (2019).CrossRefGoogle Scholar
Mannodi-Kanakkithodi, A., Treich, G.M., Huan, T.D., Ma, R., Tefferi, M., Cao, Y., Sotzing, G.A., Ramprasad, R., Adv. Mater. 28 (30), 6277 (2016).CrossRefGoogle Scholar
Meredig, B., Agrawal, A., Kirklin, S., Saal, J.E., Doak, J.W., Thompson, A., Zhang, K., Choudhary, A., Wolverton, C., Phys. Rev. B 89 (9), 094104 (2014).CrossRefGoogle Scholar
Pilania, G., Wang, C.C., Jiang, X., Rajasekaran, S., Ramprasad, R., Sci. Rep. 3, 2810 (2013).CrossRefGoogle Scholar
Rajan, K., Annu. Rev. Mater. Res. 45, 153 (2015).CrossRefGoogle Scholar
Ren, F., Ward, L., Williams, T., Laws, K.J., Wolverton, C., Hattrick-Simpers, J., Mehta, A., Sci. Adv. 4, 4 (2018).CrossRefGoogle Scholar
Seko, A., Togo, A., Hayashi, H., Tsuda, K., Chaput, L., Tanaka, I., Phys. Rev. Lett. 115 (20), 205901 (2015).CrossRefGoogle Scholar
Ueno, T., Rhone, T.D., Hou, Z., Mizoguchi, T., Tsuda, K., Mater. Discov. 4, 18 (2016).Google Scholar
Xue, D., Balachandran, P.V., Hogden, J., Theiler, J., Xue, D., Lookman, T., Nat. Commun. 7, 11241 (2016).Google Scholar
Xue, D., Balachandran, P.V., Yuan, R., Hu, T., Qian, X., Dougherty, E.R., Lookman, T., Proc. Nat. Acad. Sci. U.S.A. 113 (47), 13301 (2016).Google Scholar
Yamawaki, M., Ohnishi, M., Ju, S.H., Shiomi, J., Sci. Adv. 4, 6 (2018).CrossRefGoogle Scholar
Yuan, R., Liu, Z., Balachandran, P.V., Xue, D., Zhou, Y., Ding, X., Sun, J., Xue, D., Lookman, T., Adv. Mater. 30, 7 (2018).Google Scholar
Zhuo, Y., Tehrani, A.M., Oliynyk, A.O., Duke, A.C., Brgoch, J., Nat. Commun. 9 (1), 4377 (2018).Google Scholar
Balachandran, P.V., Xue, D., Theiler, J., Hogden, J., Gubernatis, J.E., Lookman, T., in Materials Discovery and Design, Lookman, T., Eidenbenz, S., Alexander, F., Barnes, C, Eds. (Springer, Berlin, Germany, 2018), vol. 280.Google Scholar
Kim, C., Pilania, G., Ramprasad, R., Chem. Mater. 28 (5), 1304 (2016).Google Scholar
Myung, C.W., Yun, J., Lee, G., Kim, K.S., Adv. Energy Mater. 8, 14 (2018).Google Scholar
Rosenbrock, C.W., Homer, E.R., Csanyi, G., Hart, G.L.W., npj Comput. Mater. 3, 747 (2017).CrossRefGoogle Scholar
Woods-Robinson, R., Broberg, D., Faghaninia, A., Jain, A., Dwaraknath, S.S., Persson, K.A., Chem. Mater. 30 (22), 8375 (2018).CrossRefGoogle Scholar
Dehghannasiri, R., Xue, D.Z., Balachandran, P.V., Yousefi, M.R., Dalton, L.A., Lookman, T., Dougherty, E.R., Comput. Mater. Sci. 129, 311 (2017).CrossRefGoogle Scholar
Jones, D.R., Schonlau, M., Welch, W.J., J. Glob. Optim. 13 (4), 455 (1998).CrossRefGoogle Scholar
Lookman, T., Balachandran, P.V., Xue, D.Z., Hogden, J., Theiler, J., Curr. Opin. Solid State Mater. Sci. 21 (3), 121 (2017).CrossRefGoogle Scholar
Wang, Y.F., Reyes, K.G., Brown, K.A., Mirkin, C.A., Powell, W.B., SIAM J. Sci. Comput. 37 (3), B361 (2015).Google Scholar
Bhadeshia, H.K.D.H., ISIJ Int. 39 (10), 966 (1999).CrossRefGoogle Scholar
Seko, A., Maekawa, T., Tsuda, K., Tanaka, I., Phys. Rev. B 89 (5), 054303 (2014).CrossRefGoogle Scholar
Breiman, L., Mach. Learn. 45 (1), 5 (2001).Google Scholar
Ling, J., Hutchinson, M., Antono, E., Paradiso, S., Meredig, B., Integr. Mater. Manuf. Innov. 6 (3), 207 (2017).CrossRefGoogle Scholar
Bassman, L., Rajak, P., Kalia, R.K., Nakano, A., Sha, F., Sun, J.F., Singh, D.J., Aykol, M., Huck, P., Persson, K., Vashishta, P., npj Comput. Mater. 4, 74 (2018).CrossRefGoogle Scholar
Podryabinkin, E.V., Shapeev, A.V., Comput. Mater. Sci. 140, 171 (2017).CrossRefGoogle Scholar
Xue, D., Yuan, R., Zhou, Y., Balachandran, P.V., Ding, X., Sun, J., Lookman, T., Acta Mater. 125, 532 (2017).CrossRefGoogle Scholar
Balfer, J., Bajorath, J., PLoS One 10 (3), e0119301 (2015).CrossRefGoogle Scholar
Meredig, B., Antono, E., Church, C., Hutchinson, M., Ling, J.L., Paradiso, S., Blaiszik, B., Foster, I., Gibbons, B., Hattrick-Simpers, J., Mehta, A., Ward, L., Mol. Syst. Des. Eng. 3 (5), 819 (2018).CrossRefGoogle Scholar
Acosta, M., Novak, N., Rojas, V., Patel, S., Vaish, R., Koruza, J., Rossetti, G.A., Rodel, J., Appl. Phys. Rev. 4 (4), 041305 (2017).CrossRefGoogle Scholar
Damjanovic, D., Rep. Prog. Phys. 61 (9), 1267 (1998).CrossRefGoogle Scholar
Balachandran, P.V., Shearman, T., Theiler, J., Lookman, T., Acta Crystallogr. 73 (Pt. 5), 962 (2017).Google Scholar
Grinberg, I., Suchomel, M.R., Davies, P.K., Rappe, A.M., J. Appl. Phys. 98 (9), 094111 (2005).CrossRefGoogle Scholar
Efron, B., “Bootstrap Methods: Another Look at the Jackknife,” in Breakthroughs in Statistics: Methodology and Distribution, Kotz, S., Johnson, N.L., Eds. (Springer, New York, NY, 1992), pp. 569–593.CrossRefGoogle Scholar
Smola, A.J., Scholkopf, B.A., Stat. Comput. 14 (3), 199 (2004).CrossRefGoogle Scholar
Guha, R., Van Drie, J.H., J. Chem. Inf. Model. 48 (3), 646 (2008).Google Scholar
Stumpfe, D., Bajorath, J., J. Med. Chem. 55 (7), 2932 (2012).Google Scholar
Theiler, J., Zimmer, B.G., Stat. Anal. Data Min. 10 (4), 211 (2017).CrossRefGoogle Scholar