The prediction of donor number and acceptor number of electrolyte solvent molecules based on machine learning  

作　　者：Huaping Hu Yuqing Shan Qiming Zhao Jinglun Wang Lingjun Wu Wanqiang Liu 

机构地区：[1]School of Chemistry and Chemical Engineering,Key Laboratory of Theoretical Organic Chemistry and Function Molecule of Ministry of Education,Hunan University of Science and Technology,Xiangtan 411100,Hunan,China

出　　处：《Journal of Energy Chemistry》2024年第11期374-382,共9页能源化学（英文版）

基　　金：financially National Natural Science Foundation of China (No. 22305076);Hunan Provincial Natural Science Foundation of China (No. 2022JJ30239);Scientific Research Fund of Hunan Provincial Education Department (No. 22A0328);Postgraduate Scientific Research Innovation Project of Hunan Province (No.CX20231037)。

摘　　要：Electrolyte solvents have a critical impact on the design of high performance and safe batteries.Gutmann's donor number(DN) and acceptor number(AN) values are two important parameters to screen and design superior electrolyte solvents. However, it is more time-consuming and expensive to obtain DN and AN values through experimental measurements. Therefore, it is essential to develop a method to predict DN and AN values. This paper presented the prediction models for DN and AN based on molecular structure descriptors of solvents, using four machine learning algorithms such as Cat Boost(Categorical Boosting), GBRT(Gradient Boosting Regression Tree), RF(Random Forest) and RR(Ridge Regression).The results showed that the DN and AN prediction models based on Cat Boost algorithm possesses satisfactory prediction ability, with R^(2) values of the testing set are 0.860 and 0.96. Moreover, the study analyzed the molecular structure parameters that impact DN and AN. The results indicated that TDB02m(3D Topological distance based descriptors-lag 2 weighted by mass) had a significant effect on DN, while HATS1s(leverage-weighted autocorrelation of lag 1/weighted by I-state) plays an important role in AN. The work provided an efficient approach for accurately predicting DN and AN values, which is useful for screening and designing electrolyte solvents.

关 键 词：Machine learning Donor number Acceptor number Electrolyte solvents 

分 类 号：TM912[电气工程—电力电子与电力传动] TP181[自动化与计算机技术—控制理论与控制工程]