Journal
CHEMICAL ENGINEERING SCIENCE
Volume 202, Issue -, Pages 186-193Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ces.2019.03.037
Keywords
Solvent effects; Electric double layer capacitance; Machine learning; Classical density functional theory
Categories
Funding
- National Natural Science Foundation of China [91834301, 21808055]
- National Natural Science Foundation of China for Innovative Research Groups [51621002]
- 111 Project of China [B08021]
- China Postdoctoral Science Foundation [2017M620137]
- Shanghai Sailing Program [18YF1405400, 19YF1411700]
- National Postdoctoral Program for Innovative Talents [BX201700076]
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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.
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