Solid-state batteries (SSBs) are pivotal for sustainable energy storage, delivering extended life span, low-temperature resilience, and enhanced safety. However, designing stable solid electrolytes and interfaces in SSBsremains a formidable challenge. As a disruptive catalyst for paradigm shifts spanning materials discovery andenergy system redesign, artificial intelligence (AI) is unleashing unprecedented possibilities—could it be the keybreakthrough for SSB innovation? Here, we critically review the progress of AI applications in electrolyte and in-terface engineering, covering key aspects such as stability, conductivity, mechanical properties, and interface re-sistance. This work emphasizes the integration of cutting-edge modeling strategies, including the materials’screening pipelines, machine learning force fields, and generative models. Furthermore, we conduct an in-depthanalysis of persistent challenges and propose a roadmap featuring multiscale modeling and multimodal modelswith physical constraints to build an intelligent ecosystem for SSB development. This review is expected to inspireinterdisciplinary collaborations and drive forward energy materials design, ultimately accelerating the develop-ment of sustainable and cutting-edge battery technologies.
Wang et al. (2025) studied this question.