Flexible Framework for Surface Hopping: From Hybrid Schemes for Machine Learning to Benchmarkable Nonadiabatic Dynamics
Jakub Martinka, Miko{\l}aj Martyka, Biman Medhi, Ji\v{r}\'i Pittner, Pavlo O. Dral

TL;DR
This paper introduces a flexible, open-source framework in MLatom for nonadiabatic molecular dynamics using surface hopping, facilitating machine learning integration, benchmarking, and detailed trajectory analysis.
Contribution
It presents a new implementation of surface hopping algorithms within MLatom, enabling customizable models, benchmarking, and analysis tools for nonadiabatic dynamics.
Findings
Landau--Zener scheme outperforms Baeck--An in curvature-driven surface hopping.
Custom models reduce computational time and aid benchmarking.
Framework supports detailed analysis of trajectories and ensembles.
Abstract
Nonadiabatic molecular dynamics is a key technique for investigating a broad range of photochemical and photophysical processes. Among the established approaches, surface hopping schemes are widely used and can be easily integrated with various quantum chemistry programs or machine learning models. We present a flexible framework in MLatom that includes a newly implemented Tully's fewest-switches surface hopping algorithm and its time-dependent Baeck--An variant. The capabilities of this framework are demonstrated through three representative examples corresponding to typical stages of a surface hopping study. First, we focus on methods providing energy, energy gradients and nonadiabatic couplings. We show that the flexibility of user-defined custom models can save computational time and that it is useful for benchmarking machine learning models. Next, we compare curvature-driven…
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Taxonomy
TopicsMachine Learning in Materials Science · Spectroscopy and Quantum Chemical Studies · Advanced Chemical Physics Studies
