CatRetriever: Contrastive Representation Learning for Slab-to-Bulk Retrieval in Generative Catalyst Discovery
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 heterogeneous catalysis, surface generative models have recently advanced this goal by directly generating catalyst surface-adsorbate structures. However, these models typically operate at the slab level and do not provide the corresponding parent bulk structure, making it difficult to assess bulk-dependent properties such as formation energy, surface energy, crystallographic symmetry, and synthesizability. Here, we address
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- PossiblePossibly related (embedding) · 56%Guiding generative models to uncover diverse and novel crystals via reinforcement learning →
- PossiblePossibly related (embedding) · 52%electrocatalysis-group/atomic-recipes →
- PossiblePossibly related (embedding) · 51%janosh/matbench-discovery →
- PossiblePossibly related (embedding) · 45%Efficient and valid large molecule generation via self-supervised generative models - Nature →
- PossiblePossibly related (embedding) · 45%dralgroup/mlatom →
- LinkedLinked via arxiv author · 85%Jungho Oh →
“CatRetriever: Contrastive Representation Learning for Slab-to-Bulk Retrieval in Generative Catalyst Discovery”
- LinkedLinked via arxiv author · 85%Woosung Kim →
“CatRetriever: Contrastive Representation Learning for Slab-to-Bulk Retrieval in Generative Catalyst Discovery”
- LinkedLinked via arxiv author · 85%Dong Hyeon Mok →
“CatRetriever: Contrastive Representation Learning for Slab-to-Bulk Retrieval in Generative Catalyst Discovery”