46 citations · 46 across the 5 of their papers we have counts for
6 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
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…
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…
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…