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Moving boundary truncated grid method for electronic nonadiabatic dynamics.

Chun-Yaung Lu1, Tsung-Yen Lee2, Chia-Chun Chou2

  • 1Texas Advanced Computing Center, The University of Texas at Austin, Austin, Texas 78758, USA.

The Journal of Chemical Physics
|February 2, 2022
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Summary
This summary is machine-generated.

A new moving boundary truncated grid method efficiently simulates electronic nonadiabatic transitions. This computational approach accurately models wave packet dynamics across multiple dimensions, reducing calculation efforts.

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Area of Science:

  • Quantum Chemistry
  • Computational Chemistry
  • Theoretical Chemistry

Background:

  • Electronic nonadiabatic transitions are crucial in chemical dynamics.
  • Simulating these transitions requires solving coupled time-dependent Schrödinger equations (TDSEs).
  • Traditional methods face computational challenges in high-dimensional systems.

Purpose of the Study:

  • To develop and validate a moving boundary truncated grid method for studying wave packet dynamics.
  • To assess the method's efficiency and accuracy in various dimensionalities.
  • To apply the method to chemical systems like retinal photoisomerization.

Main Methods:

  • Adaptive truncated grids are used to integrate coupled TDSEs in a diabatic representation.
  • Grid points are dynamically activated/deactivated based on wave packet evolution.
  • The method is tested on 1D models, a 2D retinal isomerization model, and multidimensional systems (2D, 3D, 4D).

Main Results:

  • The truncated grid method successfully captures wave packet dynamics on potential energy surfaces.
  • Accurate simulation of electronic nonadiabatic transitions is achieved in multidimensional systems.
  • Significant reduction in computational cost is demonstrated compared to traditional methods.

Conclusions:

  • The moving boundary truncated grid method is a powerful and efficient tool for simulating complex quantum dynamics.
  • It offers a viable approach for studying electronic nonadiabatic transitions in high-dimensional chemical systems.
  • This method can advance our understanding of processes like photoisomerization.