Hostname: page-component-8448b6f56d-gtxcr Total loading time: 0 Render date: 2024-04-23T21:49:23.269Z Has data issue: false hasContentIssue false
  • English
  • Français

Harnessing the Materials Project for machine-learning and accelerated discovery

Published online by Cambridge University Press:  10 September 2018

Weike Ye ,
Chi Chen ,
Shyam Dwaraknath ,
Anubhav Jain ,
Shyue Ping Ong  and
Kristin A. Persson
Weike Ye
Affiliation:
University of California, San Diego, USA; w6ye@ucsd.edu
Chi Chen
Affiliation:
University of California, San Diego, USA; chc273@eng.ucsd.edu
Shyam Dwaraknath
Affiliation:
Lawrence Berkeley National Laboratory, USA; shyamd@lbl.gov
Anubhav Jain
Affiliation:
Lawrence Berkeley National Laboratory, USA; ajain@lbl.gov
Shyue Ping Ong
Affiliation:
University of California, San Diego, USA; ongsp@eng.ucsd.edu
Kristin A. Persson
Affiliation:
University of California, Berkeley, and Lawrence Berkeley National Laboratory, USA; kapersson@lbl.gov
Get access

Abstract

Improvements in computational resources over the last decade are enabling a new era of computational prediction and design of novel materials. The resulting resources are databases such as the Materials Project (www.materialsproject.org), which is harnessing the power of supercomputing together with state-of-the-art quantum mechanical theory to compute the properties of all known inorganic materials, to design novel materials, and to make the data available for free to the community, together with online analysis and design algorithms. The current release contains data derived from quantum mechanical calculations for more than 70,000 materials and millions of associated materials properties. The software infrastructure carries out thousands of calculations per week, enabling screening and predictions for both novel solids as well as molecular species with targeted properties. As the rapid growth of accessible computed materials properties continues, the next frontier is harnessing that information for automated learning and accelerated discovery. In this article, we highlight some of the emerging and exciting efforts, and successes, as well as current challenges using descriptor-based and machine-learning methods for data-accelerated materials design.

Keywords

defectselastic properties
Type
Data-Centric Science for Materials Innovation
Information
MRS Bulletin , Volume 43 , Issue 9: Data-Centric Science for Materials Innovation , September 2018 , pp. 664 - 669
Copyright
Copyright © Materials Research Society 2018 

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.)

Footnotes

*

denotes equal contribution.

References

Lecun, Y., Bengio, Y., Hinton, G., Nature 521, 436 (2015).CrossRefGoogle 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, 011002 (2013).CrossRefGoogle Scholar
Bergerhoff, G., Hundt, R., Sievers, R., Brown, I.D., J. Chem. Inf. Comput. Sci. 23, 66 (1983).CrossRefGoogle Scholar
Kirklin, S., Saal, J.E., Meredig, B., Thompson, A., Doak, J.W., Aykol, M., Ruhl, S., Wolverton, C., NPJ Comput. Mater. 1, 15010 (2015).CrossRefGoogle Scholar
Curtarolo, S., Setyawan, W., Wang, S., Xue, J., Yang, K., 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).CrossRefGoogle Scholar
Seko, A., Togo, A., Hayashi, H., Tsuda, K., Chaput, L., Tanaka, I., Phys. Rev. Lett. 115, 205901 (2015).CrossRefGoogle Scholar
de Jong, M., Chen, W., Geerlings, H., Asta, M., Persson, K.A., Sci. Data 2, 150053 (2015).CrossRefGoogle Scholar
de Jong, M., Chen, W., Angsten, T., Jain, A., Notestine, R., Gamst, A., Sluiter, M., Ande, C.K., van Der Zwaag, S., Plata, J.J., Toher, C., Curtarolo, S., Ceder, G., Persson, K.A., Asta, M., Sci. Data 2, 150009 (2015).CrossRefGoogle Scholar
Carrete, J., Li, W., Mingo, N., Wang, S.D., Curtarolo, S., Phys. Rev. X 4, 011019 (2014).Google Scholar
Ricci, F., Chen, W., Aydemir, U., Snyder, G.J., Rignanese, G.-M., Jain, A., Hautier, G., Sci. Data 4, 170085 (2017).CrossRefGoogle Scholar
Ong, S.P., Cholia, S., Jain, A., Brafman, M., Gunter, D., Ceder, G., Persson, K.A., Comput. Mater. Sci. 97, 209 (2015).CrossRefGoogle Scholar
Ong, S.P., Richards, W.D., Jain, A., Hautier, G., Kocher, M., Cholia, S., Gunter, D., Chevrier, V.L., Persson, K.A., Ceder, G., Comput. Mater. Sci. 68, 314 (2013).CrossRefGoogle Scholar
Young, S.S., Yuan, F., Zhu, M., Mol. Inform. 31, 707 (2012).CrossRefGoogle Scholar
Seko, A., Togo, A., Tanaka, I., Nanoinformatics 3, 23 (2018).Google Scholar
Ward, L., Agrawal, A., Choudhary, A., Wolverton, C., NPJ Comput. Mater. 2, 16028 (2016).CrossRefGoogle Scholar
Seko, A., Maekawa, T., Tsuda, K., Tanaka, I., Phys. Rev. B Condens. Matter Mater. Phys. 89, 1 (2014).CrossRefGoogle Scholar
Carrete, J., Mingo, N., Wang, S., Curtarolo, S., Adv. Funct. Mater. 24, 7427 (2014).CrossRefGoogle Scholar
Wu, H., Lorenson, A., Anderson, B., Witteman, L., Wu, H., Meredig, B., Morgan, D., Comput. Mater. Sci. 134, 160 (2017).CrossRefGoogle Scholar
de Jong, M., Chen, W., Notestine, R., Persson, K., Ceder, G., Jain, A., Asta, M., Gamst, A., Sci. Rep. 6, 34256 (2016).CrossRefGoogle Scholar
Legrain, F., Carrete, J., van Roekeghem, A., Curtarolo, S., Mingo, N., Chem. Mater. 29, 6220 (2017).CrossRefGoogle Scholar
Gaultois, M.W., Oliynyk, A.O., Mar, A., Sparks, T.D., Mulholland, G.J., Meredig, B., APL Mater. 4 (5), 05312 (2016).CrossRefGoogle Scholar
Isayev, O., Oses, C., Curtarolo, S., Tropsha, A., Nat. Commun. 8, 15679 (2017).CrossRefGoogle Scholar
Isayev, O., Fourches, D., Muratov, E.N., Oses, C., Rasch, K., Tropsha, A., Curtarolo, S., Chem. Mater. 27, 735 (2015).CrossRefGoogle Scholar
Bartók, A.P., Payne, M.C., Kondor, R., Csányi, G., Phys. Rev. Lett. 104, 1 (2010).CrossRefGoogle Scholar
Bartók, A.P., Kondor, R., Csányi, G., Phys. Rev. B Condens. Matter Mater. Phys. 87, 1 (2013).Google Scholar
Huo, H., Rupp, M., (2017), https://www.researchgate.net/profile/Matthias_Rupp/publication/316429137_Unified_Representation_for_Machine_Learning_of_Molecules_and_Crystals/links/597ba6ff0f7e9b880293f6bf/Unified-Representation-for-Machine-Learning-of-Molecules-and-Crystals.pdf.Google Scholar
Behler, J., Parrinello, M., Phys. Rev. Lett. 98, 146401 (2007).CrossRefGoogle Scholar
Faber, F., Lindmaa, A., von Lilienfeld, O.A., Armiento, R., Int. J. Quantum Chem. 1 (115), 1094 (2015).CrossRefGoogle Scholar
Pham, T.L., Kino, H., Terakura, K., Miyake, T., Takigawa, I., Tsuda, K., Dam, H.C., Sci. Technol. Adv. Mater. 18 (1), 756 (2017).CrossRefGoogle Scholar
Ghiringhelli, L.M., Vybiral, J., Levchenko, S.V., Draxl, C., Scheffler, M., Phys. Rev. Lett. 114 (10), 105503 (2015).CrossRefGoogle Scholar
Ramprasad, R., Batra, R., Pilania, G., Mannodi-Kanakkithodi, A., Kim, C., NPJ Comput. Mater. 3 (1), 54 (2017).CrossRefGoogle Scholar
Chandrashekar, G., Sahin, F., Comput. Electr. Eng. 40, 16 (2014).CrossRefGoogle Scholar
Le, Q.V., Proc. 2013 IEEE Int. Conf. Acoust. Speech Signal Process. (2013), p. 8595.Google Scholar
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L., 2009 IEEE Conf. Comput. Vis. Pattern Recognit. (2009), p. 248.CrossRefGoogle Scholar
Hutchinson, M.L., Antono, E., Gibbons, B.M., Paradiso, S., Ling, J., Meredig, B., ArXiv preprint arXiv:1711.05099 (2017).Google Scholar
Faber, F.A., Lindmaa, A., Von Lilienfeld, O.A., Armiento, R., Phys. Rev. Lett. 117, 135502 (2016).CrossRefGoogle Scholar
Sun, W., Dacek, S.T., Ong, S.P., Hautier, G., Jain, A., Richards, W.D., Gamst, A.C., Persson, K.A., Ceder, G., Sci. Adv. 2, e1600225 (2016).CrossRefGoogle Scholar
Hautier, G., Ong, S.P., Jain, A., Moore, C.J., Ceder, G., Phys. Rev. B Condens. Matter Mater. Phys. 85, 155208 (2012).CrossRefGoogle Scholar
Wang, L., Maxisch, T., Ceder, G., Phys. Rev. B Condens. Matter 73, 195107 (2006).CrossRefGoogle Scholar
Tran, F., Blaha, P., Phys. Rev. Lett. 102, 226401 (2009).CrossRefGoogle Scholar
Chan, M.K.Y., Ceder, G., Phys. Rev. Lett. 105, 196403 (2010).CrossRefGoogle Scholar
Heyd, J., Scuseria, G.E., Ernzerhof, M., J. Chem. Phys. 118, 8207 (2003).CrossRefGoogle Scholar
Fuchs, F., Furthmüller, J., Bechstedt, F., Shishkin, M., Kresse, G., Phys. Rev. B Condens. Matter 76, 115109 (2007).CrossRefGoogle Scholar
Lee, J., Seko, A., Shitara, K., Nakayama, K., Tanaka, I., Phys. Rev. B Condens. Matter 93, 115104 (2016).CrossRefGoogle Scholar
Pugh, S.F., London Edinburgh Dublin Philos. Mag. J. Sci. 45, 823 (1954).CrossRefGoogle Scholar
Snyder, G.J., Toberer, E.S., Nat. Mater. 7, 105 (2008).CrossRefGoogle Scholar
Chen, C., Deng, Z., Tran, R., Tang, H., Chu, I.-H., Ong, S.P., Phys. Rev. Mater. 1, 043603 (2017).CrossRefGoogle Scholar
Zheng, C., Mathew, K., Chen, C., Chen, Y., Tang, H., Dozier, A., Kas, J.J., Vila, F.D., Rehr, J.J., Piper, L.F.J., Persson, K., Ong, S.P., NPJ Comput. Mater. 4 (1), 12 (2018).CrossRefGoogle Scholar
Xie, T., Grossman, J.C., Phys. Rev. Lett. 120, 145301 (2018).Google Scholar
Jain, A., Ong, S.P., Chen, W., Medasani, B., Qu, X., Kocher, M., Brafman, M., Petretto, G., Rignanese, G.-M., Hautier, G., Gunter, D., Persson, K.A., Concurr. Comput. 27, 5037 (2015).CrossRefGoogle Scholar
Mathew, K., Montoya, J.H., Faghaninia, A., Dwaraknath, S., Aykol, M., Tang, H., Chu, I.-H., Smidt, T., Bocklund, B., Horton, M., Dagdelen, J., Wood, B., Liu, Z.K., Neaton, J., Ong, S.P., Persson, K., Jain, A., Comput. Mater. Sci. 139, 140 (2017).CrossRefGoogle Scholar
Medasani, B., Gamst, A., Ding, H., Chen, W., Persson, K.A., Asta, M., Canning, A., Haranczyk, M., NPJ Comput. Mater. 2 (1), 1 (2016).CrossRefGoogle Scholar
Schmidt, J., Shi, J., Borlido, P., Chen, L., Botti, S., Marques, M.A.L., Chem. Mater. 29, 5090 (2017).CrossRefGoogle Scholar
Pilania, G., Mannodi-Kanakkithodi, A., Uberuaga, B.P., Ramprasad, R., Gubernatis, J.E., Lookman, T., Sci. Rep. 6, 19375 (2016).CrossRefGoogle Scholar
Petousis, I., Mrdjenovich, D., Ballouz, E., Liu, M., Winston, D., Chen, W., Graf, T., Schladt, T.D., Persson, K.A., Prinz, F.B., Sci. Data 4, 160134 (2017).CrossRefGoogle Scholar
Petretto, G., Dwaraknath, S., Miranda, H.P.C., Winston, D., Giantomassi, M., Van Setten, M.J., Gonze, X., Persson, K.A., Hautier, G., Rignanese, G.-M., Sci. Data 5, 180065 (2018).CrossRefGoogle Scholar
Zimmermann, N.E.R., Horton, M.K., Jain, A., Haranczyk, M., Front. Mater. 4, 1 (2017).CrossRefGoogle Scholar