文献类型：期刊文献

英文题名：Physical-chemical coupling machine learning approach to exploring reactive solvents for absorption capture of carbonyl sulfide

作者：Chen, Yuxiang[1];Liu, Chuanlei[1];Guo, Guanchu[1];Zhao, Qiyue[1];Jiang, Hao[1];Wu, Qiumin[1];Fang, Diyi[1];Gao, Weikang[1];Chen, Yu[1];Peng, Qilong[1];Wu, Kongguo[1];Shen, Benxian[1,2];Wu, Di[3,4,5,6];Cao, Fahai[1];Sun, Hui[1,2]

机构：[1]East China Univ Sci & Technol, Sch Chem Engn, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Int Joint Res Ctr Green Energy Chem Engn, Shanghai 200237, Peoples R China;[3]Washington State Univ, Alexandra Navrotsky Inst Expt Thermodynam, Pullman, WA 99163 USA;[4]Washington State Univ, Gene & Linda Voiland Sch Chem Engn & Bioengn, Pullman, WA 99163 USA;[5]Washington State Univ, Dept Chem, Pullman, WA 99163 USA;[6]Washington State Univ, Mat Sci & Engn, Pullman, WA USA

年份：2023

卷号：280

外文期刊名：CHEMICAL ENGINEERING SCIENCE

收录：;EI(收录号：20232514268447);WOS:【SCI-EXPANDED(收录号:WOS:001035648900001)】；

基金：This work is financially supported by the National Natural Science Foundation of China (Grant 21878097 and 22178109) and the Natural Science Foundation of Shanghai (Grant 21ZR1417700) . D.W. acknowledges the 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：Physical -chemical coupling; Machine learning; COS; Absorption; DFT density functional theory; & rho; density

摘要：Most of machine learning (ML) models only explain one (chemical or physical) aspect of the reaction-involved absorption and therefore fail to predict solubilities of reactive species in chemical solvents. Herein, we propose a physical-chemical coupling ML approach to exploring absorption solvents for capturing a reactive organosulfide, COS. COS solubilities for 2,824 molecules were obtained by integrating physical absorption calculated using Henry's law and chemical absorption derived from reaction equilibrium calculation. ML model of ?G of the reaction was established to examine the contributions of physical and chemical absorption. The coupling ML method was constructed by combining three absorption models. Experimental results of four commercial solvents verify that coupling ML method predicts COS solubilities in reactive and non-reactive solvents well. Furthermore, a descriptor-based molecule generation method was utilized to find 96 COSpreferred compounds. Present research highlights the coupling ML model for predicting absorption of COS and provides a general strategy for designing molecules/materials for intensified physical-chemical synergism.