46 citations · 48 across the 4 of their papers we have counts for
4 papers
Catalyst Diffusion Transformer: Generative Inverse Design of Heterogeneous Catalysts
Hayoung Doo, Dong Hyeon Mok, Seoin Back +1
The vast chemical design space and complex, interdependent design variables make catalyst discovery for targeted properties highly labor- and resource-intensive. Although generativ…
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…
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…
Catalyst design using actively learned machine with non-ab initio input features towards CO2 reduction reactions
Juhwan Noh, Jaehoon Kim, Seoin Back +1
In conventional chemisorption model, the d-band center theory (augmented sometimes with the upper edge of d-band for imporved accuarcy) plays a central role in predicting adsorptio…