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

作　　者：Jinming Fan Chao Qian Shaodong Zhou 

机构地区：[1]College of Chemical and Biological Engineering,Zhejiang Provincial Key Laboratory of Advanced Chemical Engineering Manufacture Technology,Zhejiang University,310027 Hangzhou,P.R.China [2]Institute of Zhejiang University-Quzhou,Zheda Rd.#99,324000 Quzhou,P.R.China

出　　处：《Research》2023年第4期765-775,共11页研究（英文）

基　　金：We acknowledge the generous financial support by the Zhejiang Provincial“Jianbing”and“Lingyan”R&D Programs(2023C01102,2023C01208).

摘　　要：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.

关 键 词：prediction absorption PROPERLY 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]