Data Analysis and Prediction of Energy Storage Performance in Polymer Composite Dielectrics Based on Machine Learning

In recent years, machine learning, as a new way of data analysis, has made excellent achievements in the fields of electricity, materials and chemistry. In the field of the energy storage dielectric materials, the maximum energy storage density of the composite can be greatly increased by adding nanofillers to the polyvinylidene fluoride (PVDF). In this study, machine learning was used to explore and establish the corresponding relationship between the fillers (micro information) and the energy storage performance (macro performance) of the composite dielectrics. First, 165 energy storage characteristic parameters of composites were collected to establish a database, and the characteristics of the filling phase material were taken as input descriptors (including inherent descriptors and selective descriptors). Then, the original data were processed and the dataset labels were divided according to the maximum energy storage density promotion multiple of the composites. In order to achieve the purpose of both prediction accuracy and accuracy, data sets of dichotomous classification, triple classification and quadruple classification were set respectively, and three machine learning algorithms were used to train the data sets. Finally, 11 sets of new data input training models are verified, among which 7 sets of data can be correctly predicted and classified, proving the reliability of machine learning method in the study of high energy storage density composite media. In this study, the frontier results of cross-disciplines are applied to the research field of composite dielectrics, and the database and training model established will accelerate the discovery of high-performance composites. © 2022, High Voltage Engineering Editorial Department of CEPRI. All right reserved.