Prototype-guided transfer of sparse literature knowledge for electrolyte additive discovery

2026-09-02

AI Summary

针对电解质添加剂发现中实验验证分子稀少的问题，本文提出了一种基于文献知识的原型引导分子智能方法ProtoMI，通过图对比学习和半监督学习从少量已报道分子中学习结构先验并应用于大量未标记候选物筛选。

Abstract

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.

Problem

• electrolyte additive discovery
• sparse literature knowledge
• chemical space
• lithium-ion batteries
• coupled interfacial reactions

Innovation

• Prototype-guided molecular intelligence
• Graph contrastive learning
• Semi-supervised contrastive learning
• Electrolyte additives
• Lithium-ion batteries