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AIonopedia：编排多模态学习的 LLM agent 用于离子液体发现
该 arXiv 论文提出 AIonopedia，作者称其为首个面向离子液体（IL）发现的 LLM agent。系统由面向 IL 的 LLM 增强多模态领域基础模型驱动，可进行性质预测，并引入分层搜索架构用于分子筛选与设计。作者称其在新构建的 IL 数据集上取得更优性能，在文献报道体系上可完成有效的 IL 改性，并通过真实湿实验验证，在具有挑战性的分布外任务上展现出泛化能力。摘要未披露具体基线、数据规模与湿实验细节，'首个'为作者自述，实际效果仍需以正文与复现为准。
AI推荐理由 arXiv 预印本，作者与摘要信息完整，事实明确、信息密度较高。核心新信息是提出 AIonopedia——据作者称为首个面向离子液体（IL）发现的 LLM agent，结合多模态领域基础模型做性质预测，并采用分层搜索架构进行分子筛选与设计；作者称在新构建的 IL 数据集上性能更优，并在文献体系与真实湿实验验证中表现出对分布外任务的泛化能力。局限在于：'首个'为作者自述，湿实验细节、对比基线与数据规模在摘要中未给出，效果需以论文正文与复现为准。对 AI for Science、LLM agent 与材料/化学发现方向有实际参考价值。
78
正文
Computer Science > Artificial Intelligence
arXiv:2511.11257 (cs)
[Submitted on 14 Nov 2025]
Title:AIonopedia: an LLM agent orchestrating multimodal learning for ionic liquid discovery
Authors:Yuqi Yin, Yibo Fu, Siyuan Wang, Peng Sun, Hongyu Wang, Xiaohui Wang, Lei Zheng, Zhiyong Li, Zhirong Liu, Jianji Wang, Zhaoxi Sun
View a PDF of the paper titled AIonopedia: an LLM agent orchestrating multimodal learning for ionic liquid discovery, by Yuqi Yin and 10 other authors
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Abstract: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.
Subjects:
Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG)
Cite as:
arXiv:2511.11257 [cs.AI]
(or
arXiv:2511.11257v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2511.11257
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arXiv-issued DOI via DataCite
Submission history
From: Yuqi Yin [view email]
[v1]
Fri, 14 Nov 2025 12:53:57 UTC (14,876 KB)
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