Electrochemical Model

Electrochemical models aim to accurately simulate the complex processes within electrochemical systems, such as batteries and biosensors, primarily to improve performance, safety, and lifespan. Current research emphasizes integrating physics-based models with machine learning techniques, employing architectures like neural networks (including physics-informed neural networks and model-integrated neural networks) and Bayesian optimization to enhance parameter identification and predictive accuracy. These advancements are crucial for optimizing battery charging strategies, accelerating materials discovery, and enabling more precise control and monitoring of electrochemical devices.