Preprint
Aug 2026
Compiling Chemical Knowledge into Executable Descriptors for Materials Prediction
CRISP is introduced, a large language model-assisted framework that treats representation construction as a rule-space exploration and compilation problem: it repeatedly samples target-relevant chemical rules without access to structures, labels or data splits, consolidates related concepts, and compiles each into an executable scalar descriptor supplied to a conventional learner.
Jaehwan Choi, Kunik Jang, Seongmin Kim et al.
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