Machine Learning X-ray Spectra: Theoretical Training for Experimental Predictions

The energy dependent X-ray absorption coefficient encodes the electronic and real space structure of select element species within a material. We have found that the neural network (NN) is capable of modeling this relationship in X-ray absorption near edge (XANES) and extended fine structure (EXAFS), resulting in a powerful analytic tool. To date, our NN-assisted analysis methods have been used to investigate the structure and dynamics of nanoparticles and oxide clusters as small at 4 atoms. The availability of large amounts of reliable training data, in terms of both labeling and quality, is key to an accurate NN model. For XANES and EXAFS, a database of such is not experimentally obtainable, so we must use ab initio spectroscopy codes to create a theoretical database. Because theoretical training data only approximates real observations (due to theoretical limitations, and experimental considerations such as noise, resolution, and glitching), many local minima emerge when optimizing for accurate experimental predictions. We will discuss how, by probing various local minima during training, with a set of labeled experimental data, we can find a NN which generalizes theoretical features for use in experimental predictions.