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

Key Points

• The research aims to develop a machine learning approach to analyze XANES data for predicting silicon valence and local structure.
• Utilized deep learning techniques on Si K-edge XANES spectra.
• Focused on amorphous silicon suboxides (SiO x).
• Analyzed ensemble-averaged spectra for predictions.
• Successfully predicted Si valence from the XANES analysis.
• Identified local structural characteristics of amorphous silicon suboxides.
• Demonstrated a clear relationship between composition, structure, and properties.