Transfer learning across different photocatalytic organic reactions
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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-58687-5 |
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| _version_ | 1850201068646957056 |
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| author | Naoki Noto Ryuga Kunisada Tabea Rohlfs Manami Hayashi Ryosuke Kojima Olga García Mancheño Takeshi Yanai Susumu Saito |
| author_facet | Naoki Noto Ryuga Kunisada Tabea Rohlfs Manami Hayashi Ryosuke Kojima Olga García Mancheño Takeshi Yanai Susumu Saito |
| author_sort | Naoki Noto |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e3f45b796d3e498fa0e0033c317663c8 |
| institution | OA Journals |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-e3f45b796d3e498fa0e0033c317663c82025-08-20T02:12:07ZengNature PortfolioNature Communications2041-17232025-04-0116111110.1038/s41467-025-58687-5Transfer learning across different photocatalytic organic reactionsNaoki Noto0Ryuga Kunisada1Tabea Rohlfs2Manami Hayashi3Ryosuke Kojima4Olga García Mancheño5Takeshi Yanai6Susumu Saito7Integrated Research Consortium on Chemical Sciences (IRCCS), Nagoya UniversityGraduate School of Science, Nagoya UniversityOrganic Chemistry Institute, University of MünsterGraduate School of Science, Nagoya UniversityDepartment of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto UniversityOrganic Chemistry Institute, University of MünsterGraduate School of Science, Nagoya UniversityIntegrated Research Consortium on Chemical Sciences (IRCCS), Nagoya UniversityAbstract 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.https://doi.org/10.1038/s41467-025-58687-5 |
| spellingShingle | Naoki Noto Ryuga Kunisada Tabea Rohlfs Manami Hayashi Ryosuke Kojima Olga García Mancheño Takeshi Yanai Susumu Saito Transfer learning across different photocatalytic organic reactions Nature Communications |
| title | Transfer learning across different photocatalytic organic reactions |
| title_full | Transfer learning across different photocatalytic organic reactions |
| title_fullStr | Transfer learning across different photocatalytic organic reactions |
| title_full_unstemmed | Transfer learning across different photocatalytic organic reactions |
| title_short | Transfer learning across different photocatalytic organic reactions |
| title_sort | transfer learning across different photocatalytic organic reactions |
| url | https://doi.org/10.1038/s41467-025-58687-5 |
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