文献类型：期刊文献

英文题名：Machine learning models for solvent effects on electric double layer capacitance

作者：Su, Haiping[1,2];Lian, Cheng[1,2];Liu, Jichuan[3];Liu, Honglai[1,2]

机构：[1]East China Univ Sci & Technol, Shanghai Engn Res Ctr Hierarch Nanomat, State Key Lab Chem Engn, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Sch Chem & Mol Engn, Shanghai 200237, Peoples R China;[3]UCL, Dept Chem Engn, London WC1E 7JE, England

年份：2019

卷号：202

起止页码：186

外文期刊名：CHEMICAL ENGINEERING SCIENCE

收录：;EI(收录号：20191306676644);WOS:【SCI-EXPANDED(收录号:WOS:000463879900017)】；

基金：This work was sponsored by the National Natural Science Foundation of China (No. 91834301, 21808055), National Natural Science Foundation of China for Innovative Research Groups (No. 51621002), the 111 Project of China (No. B08021), the China Postdoctoral Science Foundation (2017M620137), Shanghai Sailing Program (18YF1405400, 19YF1411700), and the National Postdoctoral Program for Innovative Talents (BX201700076).

语种：英文

外文关键词：Solvent effects; Electric double layer capacitance; Machine learning; Classical density functional theory

摘要：The role of solvent molecules in electrolytes for supercapacitors, representing a fertile ground for improving the capacitive performance of supercapacitors, is complicated and has not been well understood. Here, a combined method is applied to study the solvent effects on capacitive performance. To identify the relative importance of each solvent variable to the capacitance, five machine learning (ML) models were tested for a set of collected experimental data, including support vector regression (SVR), multilayer perceptions (MLP), M5 model tree (M5P), M5 rule (M5R) and linear regression (LR). The performances of these ML models are ranked as follows: M5P > M5R > MLP > SVR > LR. Moreover, the classical density functional theory (CDFT) is introduced to yield more microscopic insights into the conclusion derived from ML models. This method, by combining machine learning, experimental and molecular modeling, could potentially be useful for predicting and enhancing the performance of electric double layer capacitors (EDLCs). (C) 2019 Elsevier Ltd. All rights reserved.