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Area of Science:

  • Computational Chemistry
  • Quantum Dynamics
  • Molecular Modeling

Background:

  • Ab initio multiple spawning (AIM) accurately describes molecular excited-state dynamics, including nonadiabatic processes and coherence effects.
  • The computational cost of propagating numerous trajectory basis functions in AIM can be prohibitive for complex systems.

Purpose of the Study:

  • To develop a computationally efficient variant of ab initio multiple spawning.
  • To reduce the numerical burden of AIM while maintaining accuracy in excited-state dynamics simulations.

Main Methods:

  • Proposed a stochastic-selection approach for ab initio multiple spawning.
  • Identified uncoupled groups of trajectory basis functions.
  • Stochastically selected one group for propagation to continue the dynamics.

Main Results:

  • The stochastic-selection method successfully reproduced results from full AIM dynamics.
  • Accuracy was maintained in cases where trajectory groups remained uncoupled throughout the simulation.
  • Demonstrated the method's potential on indole, ethylene, and protonated formaldimine systems.

Conclusions:

  • Stochastic-selection ab initio multiple spawning offers a computationally feasible alternative to full AIM.
  • This approach is particularly effective for high-dimensional problems where trajectory couplings are limited.
  • Highlights the potential for efficient and accurate excited-state dynamics studies.