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

H Hirai and T Iizawa and T Tamura and M Karasuyama and R Kobayashi and T Hirose, PHYSICAL REVIEW MATERIALS, 6, 115601 (2022).

DOI: 10.1103/PhysRevMaterials.6.115601

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.

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