Accurate, uncertainty-aware classification of molecular chemical motifs from multi-modal X-ray absorption spectroscopy

Accurate classification of molecular chemical motifs from experimental measurement is an important problem in molecular physics, chemistry and biology. In this work, we present neural network ensemble classifiers for predicting the presence (or lack thereof) of 41 different chemical motifs on small molecules from simulated C, N and O K-edge X-ray absorption near-edge structure (XANES) spectra. Our classifiers not only reach a maximum average class-balanced accuracy of 0.99 but also accurately quantify uncertainty. We also show that including multiple XANES modalities improves predictions notably on average, demonstrating a "multi-modal advantage" over any single modality. In addition to structure refinement, our approach can be generalized for broad applications with molecular design pipelines.