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 (<100), accurate prediction of maximum wavelength for single molecules is afforded with the first stage of the protocol; by contrast, previously reported machine learning (ML) methods require >1,000 samples to ensure the accuracy of prediction. Furthermore, with <500 samples, the mean square error in the prediction of full ultraviolet spectra reaches <2%; for comparison, ML models with molecular SMILES for training require a much larger dataset (>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.

A group contribution (GC) – molecule contribution (MC) – machine learning (ML) protocol for accurate prediction of absorption spectra is presented. Upon combination of ML with revised GC methods, both the maximum absorption wavelengths and the full spectra of various sorts of dyes are afforded accurately and efficiently – by using only a small data set for training. Further, by employing a MC method designed specifically for revGC and based on MC-interpretated mixing rule by, the spectra for mixtures are obtained with high accuracy. Considering that the revGC-MC-ML protocol combines chemical principles and data-driven tools, most likely, it will be proven efficient to solve molecular-property mattered problems in wider fields.

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