用数据驱动方法优化电解液，提升高负载电池容量20%
Improving Electrolyte Performance for Target Cathode Loading Using Interpretable Data-Driven Approach
- 基于图神经网络建模电解液成分与电池性能关系
- 在目标负载下使电池比容量提升20%（超越实验优化）
- 适合电池材料设计、电解液研发人员参考
为提升电池能量密度与成本效率，需提高正极活性材料负载量，但会导致内阻增大、穿梭效应及副反应加剧。本文针对一种新型多电子反应的卤素间化合物电池，采用数据驱动方法优化电解液配方。构建包含4种溶剂和4种盐的电解液体系，利用变参数电解液组成与正极负载的实验数据，训练图基深度学习模型，映射材料设计变量与电池比容量的关系。基于该模型，通过大规模筛选与可解释性分析，优化电解液配方以提升特定负载下的电池容量。结果表明，该方法使电池比容量相比实验优化提升20%，验证了其有效性。
原文摘要 · Abstract (English)
Higher loading of active electrode materials is desired in batteries, especially those based on conversion reactions, for enhanced energy density and cost efficiency. However, increasing active material loading in electrodes can cause significant performance depreciation due to internal resistance, shuttling, and parasitic side reactions, which can be alleviated to a certain extent by a compatible design of electrolytes. In this work, a data-driven approach is leveraged to find a high-performing electrolyte formulation for a novel interhalogen battery custom to the target cathode loading. An electrolyte design consisting of 4 solvents and 4 salts is experimentally devised for a novel interhalogen battery based on a multi-electron redox reaction. The experimental dataset with variable electrolyte compositions and active cathode loading, is used to train a graph-based deep learning model mapping changing variables in the battery's material design to its specific capacity. The trained model is used to further optimize the electrolyte formulation compositions for enhancing the battery capacity at a target cathode loading by a two-fold approach: large-scale screening and interpreting electrolyte design principles for different cathode loadings. The data-driven approach is demonstrated to bring about an additional 20% increment in the specific capacity of the battery over capacities obtained from the experimental optimization.
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