用大模型打造离子液体智能发现助手，打通从设计到实验的全链条
AIonopedia: an LLM agent orchestrating multimodal learning for ionic liquid discovery
- 基于多模态大模型构建智能代理，整合分子筛选与设计流程
- 在新构建数据集上实现高精度性质预测，超出文献报告系统表现
- 真实实验验证其泛化能力，可处理分布外任务加速实际研发
离子液体（ILs）的新材料发现面临性质预测数据有限、模型精度不足和工作流程割裂等挑战。本文首次提出AIonopedia，一个基于大语言模型（LLM）增强的多模态领域基础模型，用于离子液体发现。该系统通过分层搜索架构实现分子筛选与设计，并在新构建的综合性离子液体数据集上完成训练与评估，表现出卓越性能。文献报道系统的测试表明，该智能体能有效进行离子液体改造。更进一步，通过真实湿法实验验证，其在具有挑战性的分布外任务中展现出优异泛化能力，证明了其在实际离子液体发现中的高效性。
原文摘要 · Abstract (English)
The discovery of novel Ionic Liquids (ILs) is hindered by critical challenges in property prediction, including limited data, poor model accuracy, and fragmented workflows. Leveraging the power of Large Language Models (LLMs), we introduce AIonopedia, to the best of our knowledge, the first LLM agent for IL discovery. Powered by an LLM-augmented multimodal domain foundation model for ILs, AIonopedia enables accurate property predictions and incorporates a hierarchical search architecture for molecular screening and design. Trained and evaluated on a newly curated and comprehensive IL dataset, our model delivers superior performance. Complementing these results, evaluations on literature-reported systems indicate that the agent can perform effective IL modification. Moving beyond offline tests, the practical efficacy was further confirmed through real-world wet-lab validation, in which the agent demonstrated exceptional generalization capabilities on challenging out-of-distribution tasks, underscoring its ability to accelerate real-world IL discovery.
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