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

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.