AIonopedia：一个协调多模态学习的LLM智能体，用于离子液体发现

摘要

新型离子液体（ILs）的发现受限于物性预测领域的三大关键挑战：数据稀缺、模型精度不足及工作流程碎片化。我们借助大语言模型（LLM）的技术优势，开发了AIonopedia——据我们所知，这是首个用于离子液体发现的LLM智能体。该平台以LLM增强的多模态离子液体领域基础模型为核心，能够实现精准的物性预测，并采用分层搜索架构完成分子筛选与设计。基于新构建的综合性离子液体数据集进行训练与评估，我们的模型展现出卓越性能。对文献报道体系的补充测试表明，该智能体可有效执行离子液体修饰任务。超越离线实验的范畴，我们通过真实湿实验验证进一步确认了其实际效能：智能体在具有挑战性的分布外任务中展现出卓越的泛化能力，彰显其加速实际离子液体发现进程的潜力。

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.

AIonopedia：一个协调多模态学习的LLM智能体，用于离子液体发现

AIonopedia: an LLM agent orchestrating multimodal learning for ionic liquid discovery

摘要

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