Integrating Machine Learning Techniques in Supercapacitor Research: A Comprehensive Review

This study examines supercapacitors using machine learning. Each model is tested using Random Forest, Support Vector Machine, Regression, and ANN. This article examines data selection and collection procedures to assure accuracy. It also shows how doping materials, surface area, and hole size affect supercapacitor efficiency. Case examples show how machine learning may improve material quality, predict capacitance, and boost supercapacitor efficiency. Well-known examples are used to investigate machine learning methods for capacitance prediction. The graph shows the values of the Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) for several models. Research shows that ANN-based models are more correct. This piece talks about the problems that come up when you try to use machine learning to make supercapacitors work better. Instead of focusing on controlled lab conditions, think about how accurate and clear the model is, how simple and reliable the data is, and how the results apply to real-life situations. This piece talks about the newest advances in supercapacitor machine learning. It's necessary to use complex math, visual observation, self-detection of materials, and quantum computers. The study shows that machine learning could make it faster to make supercapacitors. In a busy workplace, it's important to set goals and work together.