Machine learning prediction on intermetallic compounds with implemented virtual-center-atom structural descriptor

Abstract

ABSTRACT A virtual-center-atom (VCA) structural descriptor was proposed to construct machine learning potentials which can be applied in studying alloy materials. The descriptor performs by describing the spatial features of atomic environments centered on a defined virtual atom within crystalline structure. Computational algorithms were developed to determine the position of virtual center atom. To verify the generality of this descriptor, crystal structures from both molecular dynamics trajectory and online accessible databases are utilized to check their machine learning (ML) performances. The challenge of the ML lies in increasing the transferability of the derived weight parameters to cover a broad range of structure distribution. Forces are found difficult to train the ML model under simple optimization methods. We thus suggest deep learning techniques for improving the applicability of the VCA potential. The descriptor is simple compared to other reported schemes and has promising applications in accelerating material discovery. GRAPHICAL ABSTRACT