文献类型：期刊文献

英文题名：Prediction of Electrical Conductivity of Ionic Liquids: From COSMO-RS Derived QSPR Evaluation to Boosting Machine Learning

作者：Chen, Zixin[1];Chen, Jiahui[1];Qiu, Yuxin[1];Cheng, Jie[1];Chen, Long[1];Qi, Zhiwen[1];Song, Zhen[1]

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

年份：2024

卷号：12

期号：17

起止页码：6648

外文期刊名：ACS SUSTAINABLE CHEMISTRY & ENGINEERING

收录：;EI(收录号：20241715962914);WOS:【SCI-EXPANDED(收录号:WOS:001203973600001)】；

基金：This research is supported by the National Natural Science Foundation of China (NSFC) under Grants 22208098, 22278134, and 21CAA01709. Z.S. also acknowledges the support by the Fundamental Research Funds for the Central Universities under Grant JKA01231663.

语种：英文

外文关键词：ionic liquid electrolyte; electrical conductivity; COSMO-RS derived QSPR; boosting machine learning

摘要：By virtue of their tunable physicochemical and electrochemical properties, ionic liquids (ILs) provide a promising solution for enhancing the performance and safety of batteries. Toward efficient design of IL-based electrolytes, a reliable electrical conductivity (kappa) prediction model is highly desirable. In this work, the COSMO-RS derived QSPR model and its use as a basis for developing boosting machine learning (ML) methods for the kappa prediction of ILs are systematically examined. Based on a large experimental kappa database, the overall kappa prediction performance and the description of temperature and IL structure dependencies by the COSMO-RS derived QSPR model are evaluated thoroughly. Following that, boosting ML based on two powerful ensemble algorithms, namely random forest (RF) and extreme gradient boosting (XGB), are employed to bridge the residual between experimental and QSPR predicted kappa. The value of this proposed boosting strategy is evidenced by comparing with ML without boosting and the direct QSPR predictions. The results demonstrate the notably enhanced prediction performance of the boosting ML model and identify the boosting XGB as the best option for kappa prediction.