Multitarget Generate Electrolyte Additive for Lithium Metal Batteries

作者：Xiangyang Liu, Jianchun Chu, Sa Xue, Daquan Wang, Zhuoyang Lu, Meng Zhang, Yongqi Liu, Xin Xu, Yilin Zhang, Jiangang Long, Lingjie Meng, Jiayin Yuan, Maogang He · 发表于：Advanced Materials · 年份：2025 · DOI：10.1002/adma.202502086 · 被引用次数：5 · 研究领域：Machine Learning in Materials Science、Advanced Battery Materials and Technologies、Advancements in Battery Materials

Abstract Electrolyte additives are crucial for accelerating the commercialization of lithium metal batteries (LMBs), yet designing effective additives is challenging due to the need to balance conflicting properties, such as eectrochemical performance and nonflammability. To address this challenge, a deep learning‐assisted generative model is developed for multiobjective optimization of electrolyte additives. Overcoming data scarcity, the dataset is expanded using a molecular categorization derivation method, increasing single‐property data points to 70 095 multiproperty data points. Coupled with an asynchronous limited decoder and adversarial regulation strategy for latent distribution, this approach achieved 100% generative efficiency for structurally complex and diverse molecules in vast chemical space. The method is validated by discovering 2,4‐bis(2‐fluoroethoxy) tetrafluorocyclotriphosphazene (DFEPN), a novel additive with excellent flame resistance and stable dual electrode/electrolyte interphases. In a Li||LiFePO 4 full cell with a commercial electrolyte, DFEPN enables an order of magnitude increase in capacity retention, outperforming the state‐of‐the‐art flame‐retardant additive ethoxy(pentafluoro)cyclotriphosphazene by 33%. This study offers a pathway for developing safe and reliable lithium battery electrolytes, particularly under severe data constraints, and has broader implications for advanced battery design.