Efficient Frozen Gaussian Sampling Algorithms for Nonadiabatic Quantum Dynamics at Metal Surfaces

TL;DR

This paper introduces a Frozen Gaussian Sampling algorithm for simulating nonadiabatic quantum dynamics at metal surfaces, achieving accuracy independent of system size and semiclassical parameters, validated through numerical experiments.

Contribution

The paper presents a novel FGS algorithm with proven convergence that efficiently simulates nonadiabatic dynamics at metal surfaces, outperforming classical methods.

Findings

Sample size is independent of semiclassical parameter and number of orbitals.

Algorithm accurately captures quantum effects beyond classical surface hopping.

Numerical results validate the method's effectiveness in various physical scenarios.

Abstract

In this article, we propose a Frozen Gaussian Sampling (FGS) algorithm for simulating nonadiabatic quantum dynamics at metal surfaces with a continuous spectrum. This method consists of a Monte-Carlo algorithm for sampling the initial wave packets on the phase space and a surface-hopping type stochastic time propagation scheme for the wave packets. We prove that to reach a certain accuracy threshold, the sample size required is independent of both the semiclassical parameter $\varepsilon$ and the number of metal orbitals $N$, which makes it one of the most promising methods to study the nonadiabatic dynamics. The algorithm and its convergence properties are also validated numerically. Furthermore, we carry out numerical experiments including exploring the nuclei dynamics, electron transfer and finite-temperature effects, and demonstrate that our method captures the physics which can not be captured by classical surface hopping trajectories.