From the 1 of 4 linked papers with an AI index.
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
The paper introduces CatRetriever, a contrastive representation learning model that maps catalyst slab structures to their corresponding bulk crystals, enabling accurate retrieval…
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…
MIND: AI Co-Scientist for Material Research
Geonhee Ahn, Donghyun Lee, Hayoung Doo +3
Large language models (LLMs) have enabled agentic AI systems for scientific discovery, but most approaches remain limited to textbased reasoning without automated experimental veri…