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This study introduces a flexible Python framework for nonadiabatic dynamics simulations, enabling efficient calculation of electronic structure properties. The novel hybrid interface design supports complex, multiscale simulations and adaptive workflows for excited-state dynamics.

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Area of Science:

  • Computational Chemistry
  • Theoretical Chemistry
  • Quantum Dynamics

Background:

  • Nonadiabatic dynamics simulations require efficient electronic structure calculations.
  • Developing new interfaces for these simulations is complex and time-consuming.
  • Existing frameworks may lack flexibility for multiscale and adaptive approaches.

Purpose of the Study:

  • To present a novel, flexible, and reusable Python framework for electronic structure interfaces.
  • To introduce hybrid interfaces for hierarchical and multiscale simulations.
  • To facilitate modular, flexible, and scalable software design in excited-state dynamics.

Main Methods:

  • Object-oriented programming in Python 3.
  • Development of a reusable and extendable code base for electronic structure interfaces.
  • Implementation of hybrid interfaces supporting hierarchical and nested structures for multiscale modeling.

Main Results:

  • The framework supports computation of energies, gradients, and various couplings (spin-orbit, nonadiabatic, transition dipole moments).
  • Hybrid interfaces enable multiscale approaches (e.g., QM/MM) and adaptive learning workflows.
  • Demonstrated versatility through optimizing conical intersections and refining machine learning models.

Conclusions:

  • The novel framework significantly streamlines the development of electronic structure interfaces for nonadiabatic dynamics.
  • Hybrid and nested hybrid interfaces offer a powerful approach for complex simulations and workflows.
  • This work provides a foundation for more modular and scalable software in excited-state dynamics research.