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<p>Discovering new chemical reactions is often a slow, intuition-driven process that is limited by the vastness of chemical space and the challenges of pinpointing optimal conditions. Here we introduce a data-driven strategy that integrates expert intuition with artificial intelligence (AI)-guided exploration, enabling the rapid identification of effective reaction conditions with minimal experimentation. Applying this approach, we establish a Co(IV)–enamine catalytic system for the α-polarity inversion of carbonyl compounds. A Bayesian optimization framework is used to systematically explore over one million reaction conditions, converging on an optimized system within just 63 experiments. This strategy also uncovers unexpected reactivity patterns and expands the substrate scope through AI-guided clustering analysis. By integrating human expertise with AI-driven exploration, this workflow establishes a scalable and generalizable approach for reaction discovery, offering a powerful tool for efficient and targeted development of catalytic transformations.</p><p></p>

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Heuristic data-driven approach for synergistic cobalt(IV)–enamine catalysis

  • 期刊论文

  • 发表日期：

    2025年12月1日

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  • Liang Cheng,
  • Zhenzhi Tan,
  • Zongbin Jia,
  • Qifeng Lin,
  • Qi Yang,
  • Sanzhong Luo

摘要

Discovering new chemical reactions is often a slow, intuition-driven process that is limited by the vastness of chemical space and the challenges of pinpointing optimal conditions. Here we introduce a data-driven strategy that integrates expert intuition with artificial intelligence (AI)-guided exploration, enabling the rapid identification of effective reaction conditions with minimal experimentation. Applying this approach, we establish a Co(IV)–enamine catalytic system for the α-polarity inversion of carbonyl compounds. A Bayesian optimization framework is used to systematically explore over one million reaction conditions, converging on an optimized system within just 63 experiments. This strategy also uncovers unexpected reactivity patterns and expands the substrate scope through AI-guided clustering analysis. By integrating human expertise with AI-driven exploration, this workflow establishes a scalable and generalizable approach for reaction discovery, offering a powerful tool for efficient and targeted development of catalytic transformations.