Using machine learning to predict local chemical environments from X-ray absorption spectra

X-ray absorption spectroscopy is an element-specific technique for materials characterization. Specifically, X-ray absorption near edge structure (XANES) encodes important information of the local chemical environment (LCE, e.g. coordination number, symmetry and oxidation state) of the absorber atom that is key to the understanding of the electronic and chemical properties of materials. As such, unraveling the LCE from XANES spectra is akin to solving a challenging inverse problem. Existing methods rely on empirical fingerprints, which are often qualitative or semi-quantitative and not transferable. In this study, we present a machine learning-based approach to classify the LCE's of eight 3d transition metal families from the simulated K-edge XANES of a large number of compounds. The machine learning classifier can learn important spectral features in a broad energy range without human bias and once trained, can make predictions on the fly. We found that the machine learning classifier can achieve about 85% accuracy across the wide chemical space.