1 research outputs found
Accurate, Uncertainty-Aware Classification of Molecular Chemical Motifs from Multimodal 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 achieve class-balanced accuracies of more than 0.95 but also
accurately quantify uncertainty. We also show that including multiple
XANES modalities improves predictions notably on average, demonstrating
a “multimodal advantage” over any single modality. In
addition to structure refinement, our approach can be generalized
to broad applications with molecular design pipelines