Mapping Atomic Structures and X-ray Absorption Spectra using First Principles Computations and Machine Learning

X-ray Absorption Near Edge Spectroscopy (XANES) is frequently used to unravel the local electronic structure of atoms and studying oxidation state changes. The local structure of atomic impurities such as Cu or As in CdTe-based solar cell materials, for example, can be probed using XANES. In this work, we used first principles computations to generate Cu and As K-edge XANES data for point defects and defect complexes in bulk and grain boundary structures of CdTe, as well as various relevant compounds of the impurity atoms, with the idea of capturing the structural diversity likely to be found in the solar cell material. Using a massive computational dataset derived in this fashion, we developed a machine learning (ML) framework for accurately predicting the coordination number (CN) around the central Cu or As atom by applying regression techniques ranging from Gaussian processes (GP) to random forests (RF) to neural networks (NN). NN and GP models can predict the CN with a root mean square error of < 0.05 for a dataset with a CN range of 3 to 12. We further studied the effect of noise in the XANES data on the ML models, and used the final, optimized models to predict the coordination environment in dozens of samples with measured XANES spectra.