most citedMaterials Discovery with Extreme Properties via Reinforcement Learning-Guided Combinatorial Chemistry

21 citations · 21 across the 5 of their papers we have counts for

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cs.LG2026

CatRetriever: Contrastive Representation Learning for Slab-to-Bulk Retrieval in Generative Catalyst Discovery

Jungho Oh, Woosung Kim, Dong Hyeon Mok +2

Inverse design is an emerging data-driven paradigm for efficiently navigating vast chemical spaces to discover new materials with targeted properties, and in the context of heterog…

cs.LG2026

Toward Controllable Catalyst Inverse Design via Large-Scale Autoregressive Pretraining

Dong Hyeon Mok, Jonggeol Na, Seoin Back

Inverse design of heterogeneous catalysts remains challenging because catalyst surfaces exhibit substantial structural complexity with coupled surface-adsorbate interactions across…