(Invited) Structure Inference from X-Ray Absorption Spectroscopy Guided By Theory and Machine Learning

X-ray absorption spectroscopy (XAS) is a premier element-specific experimental technique for materials characterization in electrochemistry research. Specifically, X-ray absorption near edge structure (XANES) carries rich local structural and chemical information around X-ray absorbing species, which makes it a powerful tool to probe the dynamic evolution of the structural and electronic properties of materials during electrochemical processes. However, the correlation between XANES spectral features and the underlying local structural motifs and electronic properties is obscure, which often requires prior knowledge of fingerprints of known crystals to uncover. Here we show how first principles XAS simulations can be combined with data science to decipher the structure-spectrum relationship from XANES. We demonstrate how this approach can gain insights into the structural complexity of materials.