Toward AI ecosystems for electrolyte and interface engineering in solid-state batteries.

Abstract

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 SSBs remains a formidable challenge. As a disruptive catalyst for paradigm shifts spanning materials discovery and energy system redesign, artificial intelligence (AI) is unleashing unprecedented possibilities-could it be the key breakthrough for SSB innovation? Here, we critically review the progress of AI applications in electrolyte and interface engineering, covering key aspects such as stability, conductivity, mechanical properties, and interface resistance. 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-depth analysis of persistent challenges and propose a roadmap featuring multiscale modeling and multimodal models with physical constraints to build an intelligent ecosystem for SSB development. This review is expected to inspire interdisciplinary collaborations and drive forward energy materials design, ultimately accelerating the development of sustainable and cutting-edge battery technologies.