Machine Learning-Based Prediction of Cyclic Voltammetry Behavior of Substitution of Zinc and Cobalt in BiFeO

Artificial intelligence and machine learning have become indispensable tools across various disciplines in the present century. In that way, the role of artificial intelligence and machine learning in energy storage devices was investigated. As a preliminary study, the data derived from electrochemical studies were used for the prediction. The prediction of current from cyclic voltammetry (CV) studies was undertaken for bismuth ferrite (BFO), substitution of zinc in BFO (BFZO), and substitution of cobalt in BFO composite (BFCO). CV is a vital electrochemical technique used for studying the electrochemical behavior of any material. The electrochemical study provides insights into the energy storage behavior of the material through the specific capacitance. The machine learning models, such as Artificial Neural Network (ANN), Random Forest (RF), and XGBoost (XGB), are trained and implemented to predict current at different scan rates. These models are trained and validated using the data collected from a CHI 600E electrochemical workstation. Multiple trials of experiments were performed to build the most optimum model for the material. The predicted values provide promising results and align well with the experimental data. The XGBoost, ANN and RF models perform well for the CV data set with an average testing accuracy >97%. Also, a meta-model was created using stacking of the above three machine learning models which further improved the predictive performance, achieving a slightly higher average testing accuracy of over 97.73%. The outcomes from the models can promote the development of machine learning applications in the field of electrochemistry and provide insights into optimizing supercapacitor performance and design through data-driven approaches.