文献类型：期刊文献

英文题名：Predicting the capacitance of carbon-based electric double layer capacitors by machine learning

作者：Su, Haiping[1];Lin, Sen[2];Deng, Shengwei[3];Lian, Cheng[1];Shang, Yazhuo[1];Liu, Honglai[1]

机构：[1]East China Univ Sci & Technol, Shanghai Engn Res Ctr Hierarch Nanomat, State Key Lab Chem Engn, Sch Chem & Mol Engn, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Natl Engn Res Ctr Integrated Utilizat Salt Lake R, Shanghai 200237, Peoples R China;[3]Zhejiang Univ Technol, Coll Chem Engn, Hangzhou 310014, Zhejiang, Peoples R China

年份：2019

卷号：1

期号：6

起止页码：2162

外文期刊名：NANOSCALE ADVANCES

收录：;EI(收录号：20202008672512);WOS:【SCI-EXPANDED(收录号:WOS:000472766100008)】；

基金：This work was sponsored by the National Natural Science Foundation of China (No. 91834301 and 21808055), the Shanghai Sailing Program (18YF1405400 and 19YF1411700).

语种：英文

外文关键词：Forecasting - Machine learning - Supercapacitor - Carbon - Electrochemical electrodes

摘要：Machine learning (ML) methods were applied to predict the capacitance of carbon-based supercapacitors. Hundreds of published experimental datasets are collected for training ML models to identify the relative importance of seven electrode features. This present method could be used to predict and screen better carbon electrode materials.