Engineering early prediction of supercapacitors’ cycle life using neural networks

Machine learning (ML) can replace mechanism-based solutions, such as first-principle calculation, for speeding up fundamental researches. Although ML has the benefits of representing the material's properties with critical descriptors without involving the physical/chemical mechanisms, the reliability of data-driven models remain a great challenge because of the scarcity and irregular distribution of data sets. Here, we develop several models with different input features and ML methods. We find the artificial neural network (ANN) with reasonable features that can greatly alleviate these two challenges by a case study of early prediction of supercapacitors (SCs) cycle lives. We generate a comprehensive data set consisting 88 commercial SCs cycled under different charging strategies, with widely varying cycle lives up to 10,000 cycles. Based on the ANN model, we achieve the early prediction of SCs' cycle life with the test errors less than 10.9%, only using the first 16% cycles, and such error could be further tuned by the data set. The proposed model is suitable for training widely distributed data set and has accurate early diagnosis and prediction ability for the performance of complex SC systems.