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Transfer learning across different photocatalytic organic reactions

Author

Listed:
  • Naoki Noto

    (Nagoya University)

  • Ryuga Kunisada

    (Nagoya University)

  • Tabea Rohlfs

    (University of Münster)

  • Manami Hayashi

    (Nagoya University)

  • Ryosuke Kojima

    (Kyoto University)

  • Olga García Mancheño

    (University of Münster)

  • Takeshi Yanai

    (Nagoya University
    Nagoya University)

  • Susumu Saito

    (Nagoya University
    Nagoya University)

Abstract

While seasoned organic chemists can often predict suitable catalysts for new reactions based on their past experiences in other catalytic reactions, developing this ability is costly, laborious and time-consuming. Therefore, replicating this remarkable expertize of human researchers through machine learning (ML) is compelling, albeit that it remains highly challenging. Herein, we apply a domain-adaptation-based transfer-learning (TL) approach to photocatalysis. Despite being different reaction types, the knowledge of the catalytic behavior of organic photosensitizers (OPSs) from photocatalytic cross-coupling reactions is successfully transferred to ML for a [2+2] cycloaddition reaction, improving the prediction of the photocatalytic activity compared with conventional ML approaches. Furthermore, a satisfactory predictive performance is achieved by using only ten training data points. This experimentally readily accessible small dataset can also be used to identify effective OPSs for alkene photoisomerization, thereby showcasing the potential benefits of TL in catalyst exploration.

Suggested Citation

  • Naoki Noto & Ryuga Kunisada & Tabea Rohlfs & Manami Hayashi & Ryosuke Kojima & Olga García Mancheño & Takeshi Yanai & Susumu Saito, 2025. "Transfer learning across different photocatalytic organic reactions," Nature Communications, Nature, vol. 16(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-58687-5
    DOI: 10.1038/s41467-025-58687-5
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    References listed on IDEAS

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    1. Zi-Jing Zhang & Shu-Wen Li & João C. A. Oliveira & Yanjun Li & Xinran Chen & Shuo-Qing Zhang & Li-Cheng Xu & Torben Rogge & Xin Hong & Lutz Ackermann, 2023. "Data-driven design of new chiral carboxylic acid for construction of indoles with C-central and C–N axial chirality via cobalt catalysis," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    2. Benjamin J. Shields & Jason Stevens & Jun Li & Marvin Parasram & Farhan Damani & Jesus I. Martinez Alvarado & Jacob M. Janey & Ryan P. Adams & Abigail G. Doyle, 2021. "Bayesian reaction optimization as a tool for chemical synthesis," Nature, Nature, vol. 590(7844), pages 89-96, February.
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    4. Naoki Noto & Ryuga Kunisada & Tabea Rohlfs & Manami Hayashi & Ryosuke Kojima & Olga García Mancheño & Takeshi Yanai & Susumu Saito, 2025. "Transfer learning across different photocatalytic organic reactions," Nature Communications, Nature, vol. 16(1), pages 1-11, December.
    5. Jolene P. Reid & Matthew S. Sigman, 2019. "Holistic prediction of enantioselectivity in asymmetric catalysis," Nature, Nature, vol. 571(7765), pages 343-348, July.
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    1. Naoki Noto & Ryuga Kunisada & Tabea Rohlfs & Manami Hayashi & Ryosuke Kojima & Olga García Mancheño & Takeshi Yanai & Susumu Saito, 2025. "Transfer learning across different photocatalytic organic reactions," Nature Communications, Nature, vol. 16(1), pages 1-11, December.

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