Generative Intelligence Explores Chemical Space at Ten Million Catalysts

Ruili Li; Shuoqi Zhang; Qingli Tang; Qingqing Mao; Ritankar Das; Rui Qi; Beien Zhu; Yi Gao

doi:10.1039/d6sc01469k

January 1, 2026Chemical ScienceOpen Access

Generative Intelligence Explores Chemical Space at Ten Million Catalysts

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Key Points

This research aims to enhance the discovery of catalytic materials through innovative exploration of chemical spaces.
Utilized generative intelligence algorithms to systematically explore vast chemical spaces.
Conducted theoretical and experimental methods to assess the efficacy of discovered catalysts.
Analyzed catalytic performance metrics to validate findings.
Identified over ten million potential catalytic materials within unexplored chemical spaces.
Significantly expanded the range of viable catalysts compared to traditional exploration methods.

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Implication

Demonstrates exploration of chemical space using generative intelligence for catalytic materials, indicating improved discovery efficiency.

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,...

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Cite This Study

Li et al. (2026) studied this question.

synapsesocial.com/papers/6a080acea487c87a6a40cc65 https://doi.org/10.1039/d6sc01469k

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Generative Intelligence Explores Chemical Space at Ten Million Catalysts

Key Points

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Implication

Abstract

Discussion

Cite This Study

Generative Intelligence Explores Chemical Space at Ten Million Catalysts

Key Points

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Implication

Abstract

Discussion

Cite This Study

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