Excited-state nonadiabatic dynamics in explicit solvent using machine learned interatomic potentials
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arXiv
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| Format: | Preprint |
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2025
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| author | Tiefenbacher, Maximilian X. Bachmair, Brigitta Chen, Cheng Giuseppe Westermayr, Julia Marquetand, Philipp Dietschreit, Johannes C. B. González, Leticia |
| author_facet | Tiefenbacher, Maximilian X. Bachmair, Brigitta Chen, Cheng Giuseppe Westermayr, Julia Marquetand, Philipp Dietschreit, Johannes C. B. González, Leticia |
| contents | Excited-state nonadiabatic simulations with quantum mechanics/molecular mechanics (QM/MM) are essential to understand photoinduced processes in explicit environments. However, the high computational cost of the underlying quantum chemical calculations limits its application in combination with trajectory surface hopping methods. Here, we use FieldSchNet, a machine-learned interatomic potential capable of incorporating electric field effects into the electronic states, to replace traditional QM/MM electrostatic embedding with its ML/MM counterpart for nonadiabatic excited state trajectories. The developed method is applied to furan in water, including five coupled singlet states. Our results demonstrate that with sufficiently curated training data, the ML/MM model reproduces the electronic kinetics and structural rearrangements of QM/MM surface hopping reference simulations. Furthermore, we identify performance metrics that provide robust and interpretable validation of model accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_16974 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Excited-state nonadiabatic dynamics in explicit solvent using machine learned interatomic potentials Tiefenbacher, Maximilian X. Bachmair, Brigitta Chen, Cheng Giuseppe Westermayr, Julia Marquetand, Philipp Dietschreit, Johannes C. B. González, Leticia Chemical Physics Machine Learning Excited-state nonadiabatic simulations with quantum mechanics/molecular mechanics (QM/MM) are essential to understand photoinduced processes in explicit environments. However, the high computational cost of the underlying quantum chemical calculations limits its application in combination with trajectory surface hopping methods. Here, we use FieldSchNet, a machine-learned interatomic potential capable of incorporating electric field effects into the electronic states, to replace traditional QM/MM electrostatic embedding with its ML/MM counterpart for nonadiabatic excited state trajectories. The developed method is applied to furan in water, including five coupled singlet states. Our results demonstrate that with sufficiently curated training data, the ML/MM model reproduces the electronic kinetics and structural rearrangements of QM/MM surface hopping reference simulations. Furthermore, we identify performance metrics that provide robust and interpretable validation of model accuracy. |
| title | Excited-state nonadiabatic dynamics in explicit solvent using machine learned interatomic potentials |
| topic | Chemical Physics Machine Learning |
| url | https://arxiv.org/abs/2501.16974 |