Machine Learning‐Assisted Simulations and Predictions for Battery Interfaces

As nations worldwide intensify their efforts to achieve environmental goals and reduce carbon emissions, and as the energy landscape continues to evolve, importance of advanced battery technology becomes increasingly critical. Despite significant advancements, persistent challenges at the battery interfaces—where electrode and electrolyte interactions occur—are of particular concern. These interfaces play pivotal roles in phenomena such as dendrite growth and the formation of solid–electrolyte interphases (SEI), which are crucial for the performance, longevity, and safety of batteries. Machine learning (ML), a vital subset of artificial intelligence, offers robust capabilities by autonomously identifying patterns in complex datasets, thereby enhancing the understanding of these intricate interfacial processes. This review highlights recent progress in ML‐assisted simulations and predictions at battery interfaces, illustrating how ML accelerates the research and development trajectory. By employing ML algorithms and machine vision, simulations of lithium dendrite growth, SEI formation, and interfacial dynamics can be performed. These simulations not only deepen the comprehension but also serve as a foundation for further material optimization and predication and the battery property enhancement. The aim of this review is to spur ongoing research and the application of ML to address existing challenges, thereby advancing the development of state‐of‐the‐art battery technologies.