Machine learning based prediction of MnO2 cathode discharge capacity for high-performance zinc-ion batteries

Abstract

Zinc-ion batteries (ZIBs) are considered as a cheaper, non-toxic and safer alternative to lithium-ion batteries (LIBs). Manganese dioxide (MnO₂) is one of the most viable cathode materials for aqueous electrolyte based ZIBs. The addition of different dopants in the MnO₂ cathode material can significantly change its physical properties and electrochemical performance in ZIBs. In this study, we collected about 603 papers from which we selected 57 ZIB published papers related to doped MnO₂ as a cathode material. The dataset consists of a total of eleven features (ten input features and one target) in which six features are related to battery properties and five features are related to the elemental properties of the dopants. The Pearson correlation plot is considered to investigate the correlation between different features, and it is observed that the electronegativity and first-ionization energy of the dopant have a positive relation with discharge capacity (DC). Both classification and regression treatment are applied to our dataset using different machine learning models such as XGBoost, random forest (RF), and K-nearest Neighbors. The RF model can classify DC with an accuracy of 0.72 into three predefined grades. In the regression analysis, the XGBoost model can predict DC with an R² value of 0.92. Finally, the findings of this study can be utilized to predict the performance of doped MnO₂ before synthesizing it in the laboratory.