文献类型：期刊文献

英文题名：Bridging Machine Learning and Redlich-Kister Theory for Solid-Liquid Equilibria Prediction of Binary Eutectic Solvent Systems

作者：Wang, Ruizhuan[1];Chen, Jiahui[1];Song, Zhen[1];Qi, Zhiwen[1]

机构：[1]East China Univ Sci & Technol, Sch Chem Engn, State Key Lab Chem Engn, Shanghai 200237, Peoples R China

年份：2023

卷号：62

期号：12

起止页码：5382

外文期刊名：INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

收录：;EI(收录号：20231113706669);WOS:【SCI-EXPANDED(收录号:WOS:000947234000001)】；

基金：ACKNOWLEDGMENTS This research is supported by the National Natural Science Foundation of China under Grants 22208098, 22278134, and 21CAA01709.

语种：英文

外文关键词：Bridges - Forecasting - Forestry - Linear regression - Machine components - Machine learning

摘要：Eutectic solvents (ESs) have gained significant interest in various chemical processes due to a broad spectrum of attractive properties, whereas their rational design is currently still in its infancy. To bridge this gap, Redlich-Kister (RK) theory and machine learning are linked for the solid-liquid equilibria (SLE) prediction of ES systems, which is thermodynamically the cornerstone for ES design. RK theory with two or three parameters is first evaluated by fitting experimental SLE of an extensive ES database, demonstrating that the two-parameter-based one is sufficiently reliable for eutectic behavior correlation. Three machine learning methods, namely, Random Forest, multiple linear regression, and ElasticNet, are developed for relating the parameters of RK theory to the RDKit descriptors of ES components. The SLE predictions from RK theory parametrized by the developed machine learning models are carefully evaluated and further externally examined on several recently reported ES systems.