AIonopedia：一個調度多模態學習用於離子液體發現的大型語言模型代理

摘要

新型離子液體（IL）的發現正面臨著性質預測方面的關鍵挑戰，包括數據匱乏、模型精度不足及工作流程碎片化。我們借助大型語言模型（LLM）的強大能力，推出AIonopedia——據我們所知，這是首個用於離子液體發現的LLM智能體。該系統由LLM增強的多模態離子液體領域基礎模型驅動，能夠實現精準的性質預測，並採用分層搜索架構進行分子篩選與設計。基於新構建的綜合性離子液體數據集進行訓練與評估，我們的模型展現出卓越性能。對文獻報導系統的補充評估表明，該智能體可有效執行離子液體改性任務。在突破離線測試的局限後，我們通過真實濕實驗驗證進一步確認其實用效能：該智能體在具有挑戰性的分佈外任務中表現出卓越的泛化能力，彰顯其加速現實世界離子液體發現的潛力。

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：一個調度多模態學習用於離子液體發現的大型語言模型代理

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

摘要

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