Deep learning X-ray Absorption Near Edge Spectra

The interpretation of core-level spectroscopy data, such as x-ray absorption, has long been a challenging problem. In this talk, we will discuss the use of deep learning for the interpretation of x-ray absorption near edge spectra (XANES). In this work, computed spectra using a Bethe-Salpeter Equation-based approach, of transition metal oxides, are used as the training set. Corresponding experimental data of the system will be interpreted using the trained neural networks. We will discuss, in addition, the hyperparameter tuning and optimization for bias-variance tradeoff.