From the 1 of 5 linked papers with an AI index.
5 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…
Symbolic Predicate-Guided Language Agents for Inverse Design of Perovskite Oxides
Dong Hyeon Mok, Seoin Back, Victor Fung +1
Efficient discovery of high-performance materials has been pursued through a variety of data- and AI-driven strategies, among which inverse design, generating materials from desire…
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…
Reasoning-Driven Design of Single Atom Catalysts via a Multi-Agent Large Language Model Framework
Dong Hyeon Mok, Seoin Back, Victor Fung +1
Large language models (LLMs) are becoming increasingly applied beyond natural language processing, demonstrating strong capabilities in complex scientific tasks that traditionally…