Accelerating the Discovery of Synthesizable 2D Hybrid Double Perovskites through Human–LLM Collaborative Descriptor Engineering

Abstract Two-dimensional hybrid perovskites offer exceptional structural diversity and multifunctional properties, yet their synthesis remains a major bottleneck due to low hit rates and strong reliance on empirical intuition. Here we introduce AL-SynAgent, a human-in-the-loop, ML-guided screening workflow that integrates literature-derived synthetic priors, expert-refined chemical descriptors, and active learning to assess the synthesis feasibility of two dimensional (2D) hybrid organic–inorganic rare-earth double perovskites (HORDPs). By converting LLM-assisted literature knowledge and expert chemical reasoning into system-specific computable descriptors, AL-SynAgent reduces dependence on large experimental data sets while retaining chemically interpretable decision rules. With experimental feedback from only 4.8% of candidate space, the model converged and achieved successful target-phase formation for 11 of the 14 candidates in the prospectively selected experimental set, corresponding to a hit rate of 78.6%. Screening of an enumerated virtual library of 280,000 candidates further prioritized 64,482 compositions with high predicted synthesis feasibility. Notably, three newly obtained compounds also exhibit both excellent ferroelectricity and photoluminescence, including (4-F-321)4RbEu(NO3)8, which displays an exceptionally high Curie temperature of 388 K and strong emission at 592 nm. This work establishes a practical, human-in-the-loop strategy for data-efficient prioritization of candidate compositions within chemically constrained 2D HORDP space under a standardized laboratory synthesis protocol.