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Predicting superhard materials via a machine learning informed evolutionary structure search

Publication ,  Journal Article
Avery, P; Wang, X; Oses, C; Gossett, E; Proserpio, DM; Toher, C; Curtarolo, S; Zurek, E
Published in: npj Computational Materials
December 1, 2019

The computational prediction of superhard materials would enable the in silico design of compounds that could be used in a wide variety of technological applications. Herein, good agreement was found between experimental Vickers hardnesses, Hv, of a wide range of materials and those calculated by three macroscopic hardness models that employ the shear and/or bulk moduli obtained from: (i) first principles via AFLOW-AEL (AFLOW Automatic Elastic Library), and (ii) a machine learning (ML) model trained on materials within the AFLOW repository. Because HvML values can be quickly estimated, they can be used in conjunction with an evolutionary search to predict stable, superhard materials. This methodology is implemented in the XtalOpt evolutionary algorithm. Each crystal is minimized to the nearest local minimum, and its Vickers hardness is computed via a linear relationship with the shear modulus discovered by Teter. Both the energy/enthalpy and Hv,TeterML are employed to determine a structure’s fitness. This implementation is applied towards the carbon system, and 43 new superhard phases are found. A topological analysis reveals that phases estimated to be slightly harder than diamond contain a substantial fraction of diamond and/or lonsdaleite.

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Published In

npj Computational Materials

DOI

EISSN

2057-3960

Publication Date

December 1, 2019

Volume

5

Issue

1

Related Subject Headings

  • 5104 Condensed matter physics
  • 4016 Materials engineering
  • 3407 Theoretical and computational chemistry
 

Citation

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Avery, P., Wang, X., Oses, C., Gossett, E., Proserpio, D. M., Toher, C., … Zurek, E. (2019). Predicting superhard materials via a machine learning informed evolutionary structure search. Npj Computational Materials, 5(1). https://doi.org/10.1038/s41524-019-0226-8
Avery, P., X. Wang, C. Oses, E. Gossett, D. M. Proserpio, C. Toher, S. Curtarolo, and E. Zurek. “Predicting superhard materials via a machine learning informed evolutionary structure search.” Npj Computational Materials 5, no. 1 (December 1, 2019). https://doi.org/10.1038/s41524-019-0226-8.
Avery P, Wang X, Oses C, Gossett E, Proserpio DM, Toher C, et al. Predicting superhard materials via a machine learning informed evolutionary structure search. npj Computational Materials. 2019 Dec 1;5(1).
Avery, P., et al. “Predicting superhard materials via a machine learning informed evolutionary structure search.” Npj Computational Materials, vol. 5, no. 1, Dec. 2019. Scopus, doi:10.1038/s41524-019-0226-8.
Avery P, Wang X, Oses C, Gossett E, Proserpio DM, Toher C, Curtarolo S, Zurek E. Predicting superhard materials via a machine learning informed evolutionary structure search. npj Computational Materials. 2019 Dec 1;5(1).

Published In

npj Computational Materials

DOI

EISSN

2057-3960

Publication Date

December 1, 2019

Volume

5

Issue

1

Related Subject Headings

  • 5104 Condensed matter physics
  • 4016 Materials engineering
  • 3407 Theoretical and computational chemistry