Machine learning-based XANES analysis for predicting the local structure and valence in amorphous silicon suboxides

Abstract

Silicon suboxide (SiO_x, 0 < x < 2) has attracted considerable interest across various industrial fields due to its tunable physical properties that are afforded by its compositional ratio. However, the quantitative resolution of its atomistic structure–property correlations remains challenging using conventional approaches. In this study, nine compositionally controlled amorphous SiO_x networks were generated via molecular dynamics simulations, and a comprehensive dataset of Si K-edge X-ray absorption near-edge structure (XANES) spectra was constructed using first-principles calculations. Subsequently, a deep neural network was trained to develop a model capable of directly predicting both the local silicon atom valence state and the Si–O radial distribution function from single-site XANES spectra. Systematic sub-window analysis revealed that features near the absorption edge and the main peak provided information related to the valence state, whereas precise structural predictions required information from higher-energy regions. The model trained solely on the site-resolved spectra maintained a high predictive performance when applied to composition-averaged spectra, demonstrating robustness against the diverse atomic environments encountered in experimental measurements. By enabling the direct extraction of electronic valence and local structural descriptors from a single, ensemble-averaged XANES spectrum, this approach overcomes a key bottleneck in the atomistic analysis of amorphous materials. Consequently, it offers a transferable and experimentally viable framework for quantitatively characterizing the composition–structure–property relationships of complex, multivalent, amorphous systems. Moreover, this machine learning-based XANES approach provides a transferable framework for the quantitative characterization of such systems and may facilitate the accelerated development of SiO_x-based functional materials.