Deep learning-assisted research on high-performance electrolyte for zinc-ion capacitors

Abstract

Zinc-ion capacitors (ZICs) have garnered significant attention due to their ability to balance energy density and power density. The properties of electrolytes greatly influence the electrochemical performance of ZICs, and the oxidation potential (OP) of electrolytes can be used to determine the most suitable electrolyte for ZICs. However, traditional methods for exploring OP are time-consuming and inefficient, making it difficult to effectively determine the most suitable electrolyte for ZICs. Herein, we propose a machine learning-assisted method that combines deep learning with the traditional machine learning algorithm Gradient Boosting Regression (GBR), which we refer to as DeepGBR. This method can accurately predict the OP of electrolytes even when dealing with complex features and small datasets. Furthermore, with the assistance of deep learning, the accuracy of the predictions improves as the dataset size increases. We further validated the model with four common electrolyte molecules, finding that the predicted results are highly consistent with the theoretical values. The results show that acetonitrile has a high OP, which can achieve high energy density and long-cycle stability when used as an electrolyte solvent for ZICs. This work provides a certain reference for the database modeling strategy of electrolytes for electrochemical energy storage devices.