Machine-learning assisted identification of atomic properties from X-ray spectroscopy

The determination of atomic-scale properties such local environment and spin states of functional materials is of great importance to the materials physics community, yet the difficulty in extracting these properties from characterization data such as X-ray spectroscopy poses challenges to effective data analysis. We will discuss how machine learning models are used to extract those properties from X-ray spectra. Examples include the use of random forest models for local environment prediction from X-ray absorption spectroscopy and extracting the electronic structure change of a representative Ni-Co-Mn-based cathode material through X-ray emission spectroscopy. These findings indicate that the combination of computational spectroscopy and machine learning techniques will be an invaluable resource by greatly enhancing the efficiency at which experimental X-ray spectra can be analyzed.