ArticleChemical science2026
Generative intelligence explores the chemical space of ten million catalysts.
Article in Chemical science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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8 authors.
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Abstract
Discovery of catalytic materials requires systematic exploration of vast chemical spaces. However, the scope of exploration that can be achieved using conventional theoretical and experimental methods is very limited. Herein, we present a scalable framework based on distributed generative transformers, which integrates a transformer-based generative model with a distributed, parallel generation-screening workflow. By coupling dimensionality reduction with a machine-learning-potential (MLP) model for performance prediction, we construct a catalyst structure library comprising over ten million candidates-expanding the accessible design space by two orders of magnitude relative to existing generative models. Leveraging a pretrained model followed by task-specific fine-tuning, the tailored conditional generation strategy achieves >90% validity for target adsorbates such as CH
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