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