Charting electronic-state manifolds across molecules with multi-state learning and gap-driven dynamics via efficient and robust active learning
Abstract We present a robust protocol for affordable learning of electronic states to accelerate photophysical and photochemical molecular simulations. The protocol solves several issues precluding the widespread use of machine learning (ML) in excited-state simulations. We introduce a novel physics...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-025-01636-z |
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| author | Mikołaj Martyka Lina Zhang Fuchun Ge Yi-Fan Hou Joanna Jankowska Mario Barbatti Pavlo O. Dral |
| author_facet | Mikołaj Martyka Lina Zhang Fuchun Ge Yi-Fan Hou Joanna Jankowska Mario Barbatti Pavlo O. Dral |
| author_sort | Mikołaj Martyka |
| collection | DOAJ |
| description | Abstract We present a robust protocol for affordable learning of electronic states to accelerate photophysical and photochemical molecular simulations. The protocol solves several issues precluding the widespread use of machine learning (ML) in excited-state simulations. We introduce a novel physics-informed multi-state ML model that can learn an arbitrary number of excited states across molecules, with accuracy better or similar to the accuracy of learning ground-state energies, where information on excited-state energies improves the quality of ground-state predictions. We also present gap-driven dynamics for accelerated sampling of the small-gap regions, which proves crucial for stable surface-hopping dynamics. Together, multi-state learning and gap-driven dynamics enable efficient active learning, furnishing robust models for surface-hopping simulations and helping to uncover long-time-scale oscillations in cis-azobenzene photoisomerization. Our active-learning protocol includes sampling based on physics-informed uncertainty quantification, ensuring the quality of each adiabatic surface, low error in energy gaps, and precise calculation of the hopping probability. |
| format | Article |
| id | doaj-art-04bb013473d04589a6bea6f09e7ee47c |
| institution | OA Journals |
| issn | 2057-3960 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj-art-04bb013473d04589a6bea6f09e7ee47c2025-08-20T01:51:39ZengNature Portfolionpj Computational Materials2057-39602025-05-0111111210.1038/s41524-025-01636-zCharting electronic-state manifolds across molecules with multi-state learning and gap-driven dynamics via efficient and robust active learningMikołaj Martyka0Lina Zhang1Fuchun Ge2Yi-Fan Hou3Joanna Jankowska4Mario Barbatti5Pavlo O. Dral6University of Warsaw, Faculty of ChemistryState Key Laboratory of Physical Chemistry of Solid Surfaces, Department of Chemistry, College of Chemistry and Chemical Engineering, and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Xiamen UniversityState Key Laboratory of Physical Chemistry of Solid Surfaces, Department of Chemistry, College of Chemistry and Chemical Engineering, and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Xiamen UniversityState Key Laboratory of Physical Chemistry of Solid Surfaces, Department of Chemistry, College of Chemistry and Chemical Engineering, and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Xiamen UniversityUniversity of Warsaw, Faculty of ChemistryAix Marseille University, CNRS, ICRState Key Laboratory of Physical Chemistry of Solid Surfaces, Department of Chemistry, College of Chemistry and Chemical Engineering, and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Xiamen UniversityAbstract We present a robust protocol for affordable learning of electronic states to accelerate photophysical and photochemical molecular simulations. The protocol solves several issues precluding the widespread use of machine learning (ML) in excited-state simulations. We introduce a novel physics-informed multi-state ML model that can learn an arbitrary number of excited states across molecules, with accuracy better or similar to the accuracy of learning ground-state energies, where information on excited-state energies improves the quality of ground-state predictions. We also present gap-driven dynamics for accelerated sampling of the small-gap regions, which proves crucial for stable surface-hopping dynamics. Together, multi-state learning and gap-driven dynamics enable efficient active learning, furnishing robust models for surface-hopping simulations and helping to uncover long-time-scale oscillations in cis-azobenzene photoisomerization. Our active-learning protocol includes sampling based on physics-informed uncertainty quantification, ensuring the quality of each adiabatic surface, low error in energy gaps, and precise calculation of the hopping probability.https://doi.org/10.1038/s41524-025-01636-z |
| spellingShingle | Mikołaj Martyka Lina Zhang Fuchun Ge Yi-Fan Hou Joanna Jankowska Mario Barbatti Pavlo O. Dral Charting electronic-state manifolds across molecules with multi-state learning and gap-driven dynamics via efficient and robust active learning npj Computational Materials |
| title | Charting electronic-state manifolds across molecules with multi-state learning and gap-driven dynamics via efficient and robust active learning |
| title_full | Charting electronic-state manifolds across molecules with multi-state learning and gap-driven dynamics via efficient and robust active learning |
| title_fullStr | Charting electronic-state manifolds across molecules with multi-state learning and gap-driven dynamics via efficient and robust active learning |
| title_full_unstemmed | Charting electronic-state manifolds across molecules with multi-state learning and gap-driven dynamics via efficient and robust active learning |
| title_short | Charting electronic-state manifolds across molecules with multi-state learning and gap-driven dynamics via efficient and robust active learning |
| title_sort | charting electronic state manifolds across molecules with multi state learning and gap driven dynamics via efficient and robust active learning |
| url | https://doi.org/10.1038/s41524-025-01636-z |
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