Hybrid Unsupervised/Supervised Machine Learning for Identifying Molecular Structural Fingerprints From Ensemble Property

Abstract

The ensemble properties of a system are obtained by averaging over the properties calculated for the various configurations it can have at a finite temperature and thus cannot be captured by a single molecular structure. Such ensemble properties are often important in material discovery. In designing new materials, the goal is to predict those ensemble structures that display a tailored property. However, mapping this average property to multiple structures introduces ambiguities and unreliable convergence in supervised machine learning. This presents a major obstacle in designing new materials. Here, we introduce a hybrid unsupervised/supervised learning method and demonstrate how to predict the structural parameters defining the conformers of a heterogeneous system, melanin, from its ensemble-averaged spectra. This also shows a new way to identify different structural fingerprints responsible for an ensemble-averaged superposition spectrum.