Which Algorithm Best Propagates the Meyer–Miller–Stock–Thoss Mapping Hamiltonian for Non-Adiabatic Dynamics?

Abstract

A common strategy to simulate mixed quantum-classical dynamics is by propagating classical trajectories with mapping variables, often using the Meyer−Miller−Stock−Thoss (MMST) Hamiltonian or the related spin-mapping approach. When mapping the quantum subsystem, the coupled dynamics reduce to a set of equations of motion to integrate. Several numerical algorithms have been proposed, but a thorough performance comparison appears to be lacking. Here, we compare three time-propagation algorithms for the MMST Hamiltonian: the Momentum Integral (MInt) (

Resonance Enhancement of Vibrational Polariton Chemistry Obtained from the Mixed Quantum-Classical Dynamics Simulations

“…In a polariton-mediated electron transfer (PMET) model, we found that this treatment (quantizing q̂ normalc as a ring polymer and describe the electron donor and acceptor at the state level) gives very accurate rate constant for PMET rate constant. Other more accurate trajectory-based nonadiabatic dynamics methods, such as spin-mapping variable-based partial-linearized density matrix method (spin-PLDM) , or mapping-based surface hopping approach − should also be able to generate more accurate results. Furthermore, instead of computing population dynamics and then fitting it to extract the rate constant, one can directly obtain the rate constant by computing the flux-side correlation function using trajectory-based methods. − …”