Latent space interpretation of X-ray absorption fine structure spectra by an autoencoder approach

X-ray absorption fine structure (XAFS) is a premiere technique for the characterization of nanoscale systems. In many important cases, such as nanocatalysts with low metal loading, under high pressure and/or temperature, and under in situ/operando experimental conditions, XAFS spectral quality is limited to the region near the absorption edge. That region, also known as X-ray absorption near edge structure (XANES) has information about the local environment and electronic properties of the absorbing atom that, until recently, could not be reliably extracted by “inverting” the spectrum, only – by direct modeling using a limited number of candidate structures. In this work, we applied supervised machine learning and unsupervised machine learning approach to do the quantitative analysis of structural descriptors and explore what XANES features are embedded into a “bottleneck” representation. By combining principal component analysis with the autoencoder, we find that the latent variables are linearly separable, opening the door for their subsequent interpretation in terms of structural descriptors.