Machine learning photodynamics decode multiple singlet fission channels in pentacene crystal
Abstract Crystalline pentacene is a model solid-state light-harvesting material because its quantum efficiencies exceed 100% via ultrafast singlet fission. The singlet fission mechanism in pentacene crystals is disputed due to insufficient electronic information in time-resolved experiments and intr...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-56480-y |
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| author | Zhendong Li Federico J. Hernández Christian Salguero Steven A. Lopez Rachel Crespo-Otero Jingbai Li |
| author_facet | Zhendong Li Federico J. Hernández Christian Salguero Steven A. Lopez Rachel Crespo-Otero Jingbai Li |
| author_sort | Zhendong Li |
| collection | DOAJ |
| description | Abstract Crystalline pentacene is a model solid-state light-harvesting material because its quantum efficiencies exceed 100% via ultrafast singlet fission. The singlet fission mechanism in pentacene crystals is disputed due to insufficient electronic information in time-resolved experiments and intractable quantum mechanical calculations for simulating realistic crystal dynamics. Here we combine a multiscale multiconfigurational approach and machine learning photodynamics to understand competing singlet fission mechanisms in crystalline pentacene. Our simulations reveal coexisting charge-transfer-mediated and coherent mechanisms via the competing channels in the herringbone and parallel dimers. The predicted singlet fission time constants (61 and 33 fs) are in excellent agreement with experiments (78 and 35 fs). The trajectories highlight the essential role of intermolecular stretching between monomers in generating the multi-exciton state and explain the anisotropic phenomenon. The machine-learning-photodynamics resolved the elusive interplay between electronic structure and vibrational relations, enabling fully atomistic excited-state dynamics with multiconfigurational quantum mechanical quality for crystalline pentacene. |
| format | Article |
| id | doaj-art-313c3ea168544424a96c85aed993988b |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-313c3ea168544424a96c85aed993988b2025-02-02T12:33:23ZengNature PortfolioNature Communications2041-17232025-01-0116111010.1038/s41467-025-56480-yMachine learning photodynamics decode multiple singlet fission channels in pentacene crystalZhendong Li0Federico J. Hernández1Christian Salguero2Steven A. Lopez3Rachel Crespo-Otero4Jingbai Li5Hoffmann Institute of Advanced Materials, Shenzhen Polytechnic UniversityDepartment of Chemistry, University College LondonDepartment of Chemistry and Chemical Biology, Northeastern UniversityDepartment of Chemistry and Chemical Biology, Northeastern UniversityDepartment of Chemistry, University College LondonHoffmann Institute of Advanced Materials, Shenzhen Polytechnic UniversityAbstract Crystalline pentacene is a model solid-state light-harvesting material because its quantum efficiencies exceed 100% via ultrafast singlet fission. The singlet fission mechanism in pentacene crystals is disputed due to insufficient electronic information in time-resolved experiments and intractable quantum mechanical calculations for simulating realistic crystal dynamics. Here we combine a multiscale multiconfigurational approach and machine learning photodynamics to understand competing singlet fission mechanisms in crystalline pentacene. Our simulations reveal coexisting charge-transfer-mediated and coherent mechanisms via the competing channels in the herringbone and parallel dimers. The predicted singlet fission time constants (61 and 33 fs) are in excellent agreement with experiments (78 and 35 fs). The trajectories highlight the essential role of intermolecular stretching between monomers in generating the multi-exciton state and explain the anisotropic phenomenon. The machine-learning-photodynamics resolved the elusive interplay between electronic structure and vibrational relations, enabling fully atomistic excited-state dynamics with multiconfigurational quantum mechanical quality for crystalline pentacene.https://doi.org/10.1038/s41467-025-56480-y |
| spellingShingle | Zhendong Li Federico J. Hernández Christian Salguero Steven A. Lopez Rachel Crespo-Otero Jingbai Li Machine learning photodynamics decode multiple singlet fission channels in pentacene crystal Nature Communications |
| title | Machine learning photodynamics decode multiple singlet fission channels in pentacene crystal |
| title_full | Machine learning photodynamics decode multiple singlet fission channels in pentacene crystal |
| title_fullStr | Machine learning photodynamics decode multiple singlet fission channels in pentacene crystal |
| title_full_unstemmed | Machine learning photodynamics decode multiple singlet fission channels in pentacene crystal |
| title_short | Machine learning photodynamics decode multiple singlet fission channels in pentacene crystal |
| title_sort | machine learning photodynamics decode multiple singlet fission channels in pentacene crystal |
| url | https://doi.org/10.1038/s41467-025-56480-y |
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