Predicting superhard materials via a machine learning informed evolutionary structure search
Articolo
Data di Pubblicazione:
2019
Citazione:
Predicting superhard materials via a machine learning informed evolutionary structure search / P. Avery, X. Wang, C. Oses, E. Gossett, D.M. Proserpio, C. Toher, S. Curtarolo, E. Zurek. - In: NPJ COMPUTATIONAL MATERIALS. - ISSN 2057-3960. - 5:1(2019 Sep 03), pp. 89.1-89.11.
Abstract:
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 Hv(ML) 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(ML)Teter 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.
Tipologia IRIS:
01 - Articolo su periodico
Keywords:
Hardness; carbon allotropes
Elenco autori:
P. Avery, X. Wang, C. Oses, E. Gossett, D.M. Proserpio, C. Toher, S. Curtarolo, E. Zurek
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