文献类型：期刊文献

英文题名：Interpretable Machine Learning Model for Predicting Interaction Energies between Dimethyl Sulfide and Potential Absorbing Solvents

作者：Liu, Chuanlei[1];Chen, Yuxiang[1];Guo, Guanchu[1];Zhao, Qiyue[1];Jiang, Hao[1];Wu, Kongguo[1];Peng, Qilong[1];Chen, Yu[1];Fang, Diyi[1];Shen, Benxian[2,3];Shen, Haitao[3];Wu, Di[4,5];Sun, Hui[2,3]

机构：[1]East China Univ Sci & Technol, Sch Chem Engn, Shanghai 200237, Peoples R China;[2]East China Univ Sci & China Technol, Sch Chem Engn, Shanghai 200237, Peoples R China;[3]East China Univ Sci & Technol, Int Joint Res Ctr Green Energy Chem Engn, Shanghai 200237, Peoples R China;[4]Washington State Univ, Alexandra Navrotsky Inst Expt Thermodynam, Gene & Linda Voiland Sch Chem Engn & Bioengn, Mat Sci & Engn, Pullman, WA 99163 USA;[5]Washington State Univ, Dept Chem, Pullman, WA 99163 USA

年份：2023

卷号：62

期号：12

起止页码：5274

外文期刊名：INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

收录：;EI(收录号：20231113739988);WOS:【SCI-EXPANDED(收录号:WOS:000961454300001)】；

基金：This work is financially supported by the National Natural Science Foundation of China (grant nos. 21878097 and 22178109) and the Natural Science Foundation of Shanghai (grant no. 21ZR1417700) . D.W. acknowledges institutional funds from the Gene and Linda Voiland School of Chemical Engineering and Bioengineering and the Alexandra Navrotsky Institute for Experimental Thermodynamics at Washington State University.

语种：英文

外文关键词：Computation theory - Electronic states - Forecasting - Hydrogen bonds - Machine learning - Molecules - Quantum chemistry - Quantum theory - Sulfur compounds

摘要：Non-bonding intermolecular interactions largely dominate the selective dissolution of trace species into physical solvents and, therefore, are fundamentally important to solvent development for the capture of environment-undesired com-pounds or purification of chemicals. However, acquirement of the interaction energy requires costly quantum chemical computation and still encounters a practical challenge to build a chemically interpretable machine learning (ML) prediction model using documented molecular descriptors. Herein, we report an ML model for predicting the interaction energies (Eint) between dimethyl sulfide and potential absorbing solvents. Applying the reduced density gradient and quantum theory of atoms in molecules analyses, the non-bonding intermolecular interactions of dimethyl sulfide with solvent compounds were elucidated through focusing on the molecular fragments containing the main center (MC) and secondary center (SC) rather than the whole molecule. The training data set was obtained using a molecular generation strategy, and 21 molecular descriptors were defined to describe the electronic states of the central atoms in each solvent molecule and its nearby hydrogen-bond donors. Model analysis reveals that Eint is mainly determined by the charge state of the crucial fragments and the hydrogen-bond donors of the solvent molecule. The custom-defined descriptors not only improve the regression and prediction performance but also enable the interpretability of the ML model. Additionally, the absorption equilibrium measurements of solubilities of dimethyl sulfide in several commercial solvents verified the strong correlation between the dissolving affinity and solute-solvent intermolecular interaction energy. The present study provides an approach to building practical and interpretable intelligent algorithms to aid the development of sustainable chemicals or processes.