发表机构
The Hong Kong University of Science and Technology (Guangzhou); The Hong Kong University of Science and Technology（香港科技大学（广州）; 香港科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对电解液添加剂发现中实验验证分子稀疏的问题，开发原型引导分子智能框架ProtoMI，结合文献与未标注数据筛选候选物，成功发现提升电池性能的含硼添加剂TNDB，为数据稀缺场景的分子发现提供了高效方案。
AI 中文摘要
电解液添加剂的发现仍具挑战性，因为实验验证的分子数量稀少，而可及的化学空间庞大且大部分未标注。在锂离子电池中这一挑战更为突出，添加剂的性能源于耦合的界面反应，而非单一分子属性。本文开发了一种原型引导的分子智能框架ProtoMI，该框架从已报道的电解液添加剂中学习可迁移的结构先验，并用其对未标注化学空间中的候选物进行优先级排序。对于含硼添加剂，ProtoMI结合了126种文献报道分子与179977种未标注候选物。图对比学习从已报道的添加剂中识别出7种可化学解释的原型，原型引导的半监督对比学习在源-目标分布不匹配的情况下将这些原型适配到候选空间。在回顾性时间验证中，ProtoMI的富集因子达9.2至45.6，同时仅筛选不到2%的候选空间。后续的转化步骤确定了4种可商业获取的候选物。其中一种代表性候选物4,4,5,5-四甲基-2-[10-(1-萘基)蒽-9-基]-1,3,2-二氧杂硼戊环（TNDB），在55℃下相对于基准电解液，将高温LiFePO4||石墨的循环性能提升了34.93%。一系列表征及原位光纤傅里叶变换红外光谱表明，TNDB会形成含B、F/P/O修饰的无机界面相，抑制溶剂分解并减少石墨上的Fe沉积。该案例研究表明，稀疏的文献知识可如何指导数据稀缺的电池添加剂空间中实验高效的分子发现。
英文摘要
Electrolyte additive discovery remains challenging because experimentally validated molecules are sparse, whereas accessible chemical spaces are vast and largely unlabeled. This challenge is amplified in lithium-ion batteries, where additive performance arises from coupled interfacial reactions rather than a single molecular property. Here, we develop a prototype-guided molecular intelligence, ProtoMI, a literature-driven framework that learns transferable structural priors from reported electrolyte additives and uses them to prioritize candidates in unlabeled chemical space. For boron-containing additives, ProtoMI combines 126 literature-reported molecules with 179,977 unlabeled candidates. Graph contrastive learning identifies seven chemically interpretable prototypes from the reported additives, and prototype guided semi-supervised contrastive learning adapts these prototypes to the candidate space under source-target distribution mismatch. In retrospective temporal validation, ProtoMI achieves enrichment factors of 9.2-45.6 while screening less than 2% of the candidate space. A subsequent translation step identifies four commercially accessible candidates. One representative candidate, 4,4,5,5-Tetramethyl-2-[10-(1naphthyl)anthracen-9-yl]-1,3,2-dioxaborolane (TNDB), improves high-temperature LiFePO4||graphite cycling at 55 °C by 34.93% relative to the baseline electrolyte. An arsenal of characterizations and operando optical fiber Fourier transform infrared spectroscopy suggest that TNDB forms B-containing, F/P/O-modified inorganic interphases, suppresses solvent decomposition and reduces Fe deposition on graphite. This case study shows how sparse literature knowledge can guide experimentally efficient molecular discovery in data-scarce battery-additive spaces.
Comments79 pages, 26 figures
