46 citations · 46 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…
cs.LG2024★ 46 cited
Generative Language Model for Catalyst Discovery
Dong Hyeon Mok, Seoin Back
Discovery of novel and promising materials is a critical challenge in the field of chemistry and material science, traditionally approached through methodologies ranging from trial…