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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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
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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| Summary: | 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. |
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| ISSN: | 2041-1723 |