Covalent organic frameworks (COFs) have emerged as promising candidates for solid-state electrolytes (SSEs) in lithium-ion batteries (LIBs) due to their tunable pore sizes, high surface areas, and exceptional thermal stability. However, the rational design of COF-based SSEs is hindered by the vast combinatorial chemical space, synthetic complexity, and the need for precise control over structure-property relationships. Machine learning (ML) has revolutionized the development of COF materials by enabling high-throughput screening, predictive modeling, and optimization of synthesis conditions. This review systematically explores the integration of ML in COF-based SSE development, focusing on structure prediction, synthesis-performance optimization, and the application of digital twin strategies. We highlight the role of ML in accelerating the discovery of high-performance COF-based solid-state electrolytes, optimizing ionic conductivity, and enhancing interfacial stability. By summarizing the synergistic pathways between computational simulations and experimental validation, this review offers strategic guidelines for overcoming traditional “trial-and-error” R&D bottlenecks, paving the way for the next generation of high-energy-density LIBs.
Xu et al. (2026) studied this question.