Simulating Passage through a Cascade of Conical Intersections with Collapse-to-a-Block Molecular Dynamics

TL;DR

This paper evaluates the performance of the collapse-to-a-block (TAB) molecular dynamics method in simulating nonadiabatic passage through cascades of conical intersections in dense electronic state manifolds, demonstrating its high accuracy and robustness.

Contribution

The study extends TAB molecular dynamics to two-dimensional conical intersection cascades, comparing variants and showing their accuracy against exact quantum dynamics.

Findings

All TAB variants accurately model passage through conical intersection cascades.

TAB-w provides slightly better population dynamics accuracy than original TAB.

Using approximate eigenstates introduces minimal additional error.

Abstract

The Ehrenfest with collapse-to-a-block (TAB) molecular dynamics approach was recently introduced to allow accurate simulation of nonadiabatic dynamics on many electronic states. Previous benchmarking work has demonstrated it to be highly accurate for modeling dynamics in one-dimensional analytical models, but nonadiabatic dynamics often involves conical intersections, which are inherently two-dimensional. In this report, we assess the performance of TAB on two-dimensional models of cascades of conical intersections in dense manifolds of states. Several variants of TAB are considered, including TAB-w, which is based on the assumption of a Gaussian rather than exponential decay of the coherence, and TAB-DMS, which incorporates an efficient collapse procedure based on approximate eigenstates. Upon comparison to numerically exact quantum dynamics simulations, it is found that all TAB variants provide a suitable description of the dynamical passage through a cascade of conical intersections. The TAB-w approach is found to provide a somewhat more accurate description of population dynamics than the original TAB method, with final absolute population errors $\leq$0.013 in all cases. Even when only four approximate eigenstates are computed, the use of approximate eigenstates was found to introduce minimal additional error (absolute population error $\leq$0.018 in all models).