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| Hauptverfasser: | , , , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2511.11257 |
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| _version_ | 1866915618499854336 |
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| author | Yin, Yuqi Fu, Yibo Wang, Siyuan Sun, Peng Wang, Hongyu Wang, Xiaohui Zheng, Lei Li, Zhiyong Liu, Zhirong Wang, Jianji Sun, Zhaoxi |
| author_facet | Yin, Yuqi Fu, Yibo Wang, Siyuan Sun, Peng Wang, Hongyu Wang, Xiaohui Zheng, Lei Li, Zhiyong Liu, Zhirong Wang, Jianji Sun, Zhaoxi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_11257 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AIonopedia: an LLM agent orchestrating multimodal learning for ionic liquid discovery Yin, Yuqi Fu, Yibo Wang, Siyuan Sun, Peng Wang, Hongyu Wang, Xiaohui Zheng, Lei Li, Zhiyong Liu, Zhirong Wang, Jianji Sun, Zhaoxi Artificial Intelligence Computational Engineering, Finance, and Science Machine Learning 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. |
| title | AIonopedia: an LLM agent orchestrating multimodal learning for ionic liquid discovery |
| topic | Artificial Intelligence Computational Engineering, Finance, and Science Machine Learning |
| url | https://arxiv.org/abs/2511.11257 |