Prediction of interface structures and energies via virtual screening.

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

SUBMITTER: Kiyohara S 

PROVIDER: S-EPMC5262449 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature