Research ArticleENGINEERING

Prediction of interface structures and energies via virtual screening

Science Advances  25 Nov 2016:
Vol. 2, no. 11, e1600746
DOI: 10.1126/sciadv.1600746

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Abstract

Interfaces markedly affect the properties of materials because of differences in their atomic configurations. Determining the atomic structure of the interface is therefore one of the most significant tasks in materials research. However, determining the interface structure usually requires extensive computation. If the interface structure could be efficiently predicted, our understanding of the mechanisms that give rise to the interface properties would be significantly facilitated, and this would pave the way for the design of material interfaces. Using a virtual screening method based on machine learning, we demonstrate a powerful technique to determine interface energies and structures. On the basis of the results obtained by a nonlinear regression using training data from 4 interfaces, structures and energies for 13 other interfaces were predicted. Our method achieved an efficiency that is more than several hundred to several tens of thousand times higher than that of the previously reported methods. Because the present method uses geometrical factors, such as bond length and atomic density, as descriptors for the regression analysis, the method presented here is robust and general and is expected to be beneficial to understanding the nature of any interface.

Keywords
  • Interface
  • virtual screening
  • prediction
  • atomic structure

This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

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