Probabilistic generative models for latent representation learning of X-ray absorption fine structure (XAFS) spectra

Low dimensional latent representations of data make it easier to extract hidden patterns and these representations can then be further used to build classifiers and other predictors. In this work, we will underscore the importance of applying probabilistic generative models to analyze X-ray absorption fine structure (XAFS) data and develop pathways for good latent representations. Inverting XAFS spectra to structural descriptors and mapping the evolution of changes in these descriptors during the in-situ experiment or under varying experimental conditions are of significant interest to catalysis community. By using deep learning and variational inference, we show that Variational Autoencoders (VAE) can help disentangle non-linear interactions between underlying explanatory factors. Furthermore, we would show that low dimensional latent representations can also be utilized to invert the experimentally obtained XAFS spectra to structural and electronic properties of the catalysts in Pd nanoparticles under hydrogen atmosphere at elevated temperatures.