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<p>The pursuit of autonomous chemical transformations with single-bond precision represents a central challenge in molecular nanoscience. While scanning tunneling microscopy (STM) enables site-specific reactions by directly engaging individual atoms and bonds, conventional approaches rely on expert intervention and lack reproducibility and scalability. Here we introduce a deep learning-based strategy that autonomously executes multi-step, bond-selective transformations. Our system integrates computer vision for molecular recognition, neural networks for bond-state classification, and deep reinforcement learning for closed-loop optimization of activation parameters. As a proof of concept, we demonstrate the selective dissociation of C-Br bonds in a tetra-brominated porphyrin on Au(111). Importantly, the approach extends beyond single-bond events, enabling programmed multi-step sequences including four distinct pathways with high fidelity. By advancing from isolated, human-directed manipulations to fully autonomous, data-driven reaction control, this platform establishes a paradigm for intelligent single-molecule chemistry. It provides a generalizable framework for on-surface synthesis, where adaptive agents orchestrate molecular transformations with a level of precision and scalability unattainable by manual approaches.</p>

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Deep learning drives autonomous molecular reactions with single-bond selectivity in tetra-brominated porphyrins on Au(111)

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月4日

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  • Zhiwen Zhu,
  • Qi Huang,
  • Tairan Yang,
  • Hao Jiang,
  • Shaoxuan Yuan,
  • Juan Xiang,
  • Liangliang Cai,
  • Qiang Sun

摘要

The pursuit of autonomous chemical transformations with single-bond precision represents a central challenge in molecular nanoscience. While scanning tunneling microscopy (STM) enables site-specific reactions by directly engaging individual atoms and bonds, conventional approaches rely on expert intervention and lack reproducibility and scalability. Here we introduce a deep learning-based strategy that autonomously executes multi-step, bond-selective transformations. Our system integrates computer vision for molecular recognition, neural networks for bond-state classification, and deep reinforcement learning for closed-loop optimization of activation parameters. As a proof of concept, we demonstrate the selective dissociation of C-Br bonds in a tetra-brominated porphyrin on Au(111). Importantly, the approach extends beyond single-bond events, enabling programmed multi-step sequences including four distinct pathways with high fidelity. By advancing from isolated, human-directed manipulations to fully autonomous, data-driven reaction control, this platform establishes a paradigm for intelligent single-molecule chemistry. It provides a generalizable framework for on-surface synthesis, where adaptive agents orchestrate molecular transformations with a level of precision and scalability unattainable by manual approaches.