Excited-state nonadiabatic dynamics in explicit solvent using machine learned interatomic potentials

• URL: http://arxiv.org/abs/2501.16974v1
• Date: Tue, 28 Jan 2025 14:14:43 GMT
• Title: Excited-state nonadiabatic dynamics in explicit solvent using machine learned interatomic potentials
• Authors: Maximilian X. Tiefenbacher, Brigitta Bachmair, Cheng Giuseppe Chen, Julia Westermayr, Philipp Marquetand, Johannes C. B. Dietschreit, Leticia González,
• Abstract summary: We use FieldSchNet to replace QM/MM electrostatic embedding with its ML/MM counterpart for nonadiabatic excited state trajectories. Our results demonstrate that the ML/MM model reproduces the electronic kinetics and structural rearrangements of QM/MM surface hopping reference simulations.
• Score: 0.602276990341246
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• Abstract: 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.

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