Machine-learning-based prediction of first-principles XANES spectra for amorphous materials

Abstract

In this paper, a machine-learning-based method is proposed for predicting the x-ray absorption near-edge structure (XANES) for local configurations specific to amorphous materials. A combination of molecular dynamics and first-principles XANES simulations was adopted. The XANES spectrum was assumed to be accurately represented by linear regression of the local atomic descriptors. A comprehensive prediction of Si $K$-edge XANES spectra was performed based on an atom-centered symmetry function, smooth overlap of atomic positions, local many-body tensor representation, and spectral neighbor analysis potential. Furthermore, prediction accuracy was improved by compression of XANES spectral data and efficient sampling of training data.