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 e...
Jaehwan Choi, Kunik Jang, Seongmin Kim et al.· 1 citation
This Perspective examines how recent advances in data‐driven modeling, high‐performance simulation, and autonomous experimentation are converging to accelerate the discovery of functional materials for next‐generation technologies—from energy storage and biomedicine to nanoelectronics and quantum devices.
Cristiano Malica, Kostya S. Novoselov, Seongmin Kim et al.· Advanced Intelligent Systems· 0 citations
This review examines emerging AI methodologies for accelerated materials discovery, with particular emphasis on how computational design, data infrastructure, synthesis planning, and autonomous experimentation can be connected into experimentally grounded workflows.
Jaehwan Choi, Seongmin Kim, Junkil Park et al.· Chemical Reviews· 4 citations
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