Active‐learning‐based rational inverse design of ionic liquids for interfacial property optimization

Ionic liquids (ILs) are thermally stable, nonvolatile salts widely used in catalysis and separations, yet their rational design is challenged by the vast cation‐anion combinatorial space and strongly coupled interfacial properties. In this work, we propose an inverse design framework that combines data augmentation, dual‐surrogate optimization, and active learning to enable efficient exploration of IL chemical space. A DFT‐guided augmentation strategy is employed to expand the limited training data, while a dual‐surrogate scheme guides optimization by jointly predicting interfacial properties and structural validity. To maintain reliability in unexplored regions, an active‐learning loop is introduced to continuously refine the validity classifier. Furthermore, substructure constraints derived from interpretability analysis are incorporated to allow motif‐controlled generation. Computational results show that the proposed framework achieves markedly improved optimization efficiency while consistently maintaining high structural validity, providing a practical route for the rational design of ILs with tailored interfacial properties.