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This study introduces a flexible framework for nonadiabatic molecular dynamics using surface hopping. The MLatom package accelerates machine learning model development for photochemical and photophysical processes.

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Area of Science:

  • Computational Chemistry
  • Quantum Dynamics
  • Photochemistry

Background:

  • Nonadiabatic molecular dynamics (NAMD) is crucial for understanding photochemical and photophysical processes.
  • Surface hopping schemes are widely used NAMD methods, integrable with quantum chemistry and machine learning.
  • Existing methods require efficient and flexible computational frameworks.

Purpose of the Study:

  • To present a flexible framework for NAMD in the MLatom package.
  • To implement Tully's fewest-switches surface hopping algorithm and its time-dependent Baeck-An variant.
  • To demonstrate the framework's utility through representative examples and analysis tools.

Main Methods:

  • Implementation of Tully's fewest-switches surface hopping and its time-dependent Baeck-An variant within MLatom.
  • Utilizing user-defined custom models for energy, gradients, and nonadiabatic couplings.
  • Comparison of curvature-driven surface hopping schemes, including Landau-Zener and time-dependent Baeck-An.

Main Results:

  • Demonstrated computational time savings with flexible user-defined models.
  • Showcased the utility of custom models for benchmarking machine learning models.
  • The Landau-Zener approach showed superior performance over the time-dependent Baeck-An scheme in comparisons.

Conclusions:

  • The MLatom framework facilitates accelerated development of machine learning models for NAMD.
  • The framework provides deeper insights into nonadiabatic dynamics.
  • The open-source MLatom package offers a comprehensive tool for NAMD research.