Machine Learning Orchestrating the Materials Discovery and Performance Optimization of Redox Flow Battery

Abstract

Abstract This review exploits the crucial role of computational methods in discovering and optimizing materials for redox flow batteries (RFBs). Integration of high‐throughput computational screening (HTCS) and machine learning (ML) accelerates materials discovery, guided by algorithms categorizing RFBs. A collaborative exploration, spanning macroscopic to mesoscopic scales, combines quantum machine learning with reinforcement learning, transfer learning, time series analysis, Bayesian optimization, active learning and various generative models. The collaborative integration of ML with computational techniques and experimental methods, anchored in experimentally validated Density Functional Theory (DFT) calculations and molecular dynamics (MD) simulations, proves indispensable for cost‐effective RFBs. Data collection and feature engineering are explored, emphasizing the integration of optimization goals and precise data collection within the ML framework. Feature analysis importance is highlighted, utilizing methods such as the filter, embedded, wrapper and deep learning methods for efficient energy materials exploration. Computational perspectives on materials features and operating conditions encompass membrane characteristics, fluid dynamics, temperature dependence and pressure sensitivity. Time‐dependent features and ML‐generated insights are crucial for understanding cycling performance intricacies, providing a comprehensive understanding of RFB materials.

Keywords

• Redox flow batteries
• Computational chemistry
• Integrated
• Materials discovery
• Performance optimization