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

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