Combining machine learning and XANES spectra featurization to make chemical environment predictions of CdTe materials

X-ray absorption spectroscopy is a popular method for unraveling the local atomistic and electronic structures of materials. This approach can be used for studying the bonding environment of various dopants in CdTe photovoltaics, which is important for understanding and improving their performance. In this work, we exploit the relationships between x-ray absorption near edge structure (XANES) features and local atomic structures to develop a machine learning (ML) framework for accurately predicting the coordination numbers of dopants in CdTe.  Using FEFF9 simulations, we generated a large dataset of chemical environments that sample dozens of compounds focusing on Cu and As dopants, including bulk phases of Te- and Se-based structures, As and Cu defects in CdTe, etc. Simulated XANES are mapped to the coordination environment using random forest regression, neural network, and gaussian process regression models. The models are trained on the spectra in addition to other features such as its first and second derivative. The best performing ML models are deployed for predictions on measured XANES from a set of experimental samples, showing the utility and wide applicability of this approach.