Machine Learning Spectroscopy Using a 2-Stage, Generalized Constituent Contribution Protocol

Abstract

A corrected group contribution (CGC)–molecule contribution (MC)–Bayesian neural network (BNN) protocol for accurate prediction of absorption spectra is presented. Upon combination of BNN with CGC methods, the full absorption spectra of various molecules are afforded accurately and efficiently—by using only a small dataset for training. Here, with a small training sample (1,000 samples to ensure the accuracy of prediction. Furthermore, with 2,000) to achieve comparable accuracy. Moreover, by employing an MC method designed specifically for CGC that properly interprets the mixing rule, the spectra of mixtures are obtained with high accuracy. The logical origins of the good performance of the protocol are discussed in detail. Considering that such a constituent contribution protocol combines chemical principles and data-driven tools, most likely, it will be proven efficient to solve molecular-property-relevant problems in wider fields.