AI-driven ionic liquid discovery with unified chemical intelligence

Abstract:The rational discovery of functional solvents remains challenging because macroscopic solvent behaviors emerge from complex many-body interactions across vast chemical spaces. Ionic Liquids (ILs) constitute one of the most chemically diverse solvent families, yet their exceptional structural tunability remains only partially explored owing to the limitations of existing experimental and computational approaches. Here we introduce AIonopedia, a large-language-model-orchestrated agentic framework for IL research. Built around a domain-specific multimodal foundation model, it integrates information retrieval, chemical reasoning and autonomous tool execution within a unified architecture. Pretrained on large-scale unlabeled corpora and fine-tuned using over 80,000 curated measurements, AIonopedia learns expressive joint representations of multicomponent ionic systems, enabling accurate prediction across diverse physicochemical properties. Through a series of literature-grounded evaluations, AIonopedia exhibits robust out-of-distribution generalization, while many-body-expansion-based analysis provides physically grounded explanations of structure-property relationships. By coupling molecular screening and optimization with wet-lab validation in a closed-loop workflow, AIonopedia identifies high-performance ILs and reveals transferable design principles for the capture of various volatile organic compounds. This work establishes an end-to-end AI-driven paradigm for understanding and exploring complex ionic systems, demonstrating the promise of chemically specialized foundation models as a generalizable strategy for scientific discovery.

Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG)

arXiv:2511.11257 [cs.AI]

(or arXiv:2511.11257v2 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2511.11257 Focus to learn more arXiv-issued DOI via DataCite

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