Data-Driven Advances in Electrochemical Energy and Sensing: Artificial Intelligence Approaches from Concept to Application

Abstract

The intersection of machine learning (ML) and electrochemical research is catalyzing transformative advancements in energy storage and conversion technologies. This review critically examines the role of ML and deep learning (DL) in optimizing electrochemical systems, focusing on batteries, supercapacitors, fuel cells, and sensors. ML-driven approaches facilitate accelerated material discovery, precise property predictions, and enhanced device performance monitoring, surpassing conventional trial-and-error methodologies. Integrating computational materials science, including density functional theory (DFT) and molecular dynamics (MD), with ML enables predictive modelling of electrochemical processes at an unprecedented scale. However, challenges such as data heterogeneity, model interpretability, and computational cost continue to limit widespread adoption. This review identifies key strategies to overcome these barriers, including establishing standardized data repositories, developing hybrid physics-informed ML models, and implementing explainable AI (XAI) for enhanced model transparency. By addressing these challenges, ML has the potential to drive the next wave of breakthroughs in electrochemical energy storage and conversion, accelerating the transition toward a sustainable and energy-secure future.