论文标题
金属表面上非绝热量子动力学的有效冷冻高斯抽样算法
Efficient Frozen Gaussian Sampling Algorithms for Nonadiabatic Quantum Dynamics at Metal Surfaces
论文作者
论文摘要
在本文中，我们提出了一种冷冻的高斯采样（FGS）算法，用于模拟具有连续光谱的金属表面上的非绝热量子动力学。该方法由一种用于对波数据包的蒙特卡罗算法进行采样，用于对相空间上的初始波数据包进行采样和表面跳动的随机时间传播方案。我们证明，要达到一定的准确性阈值，所需的样本量独立于半经典参数$ \ varepsilon $和金属轨道$ n $的数量，这使其成为研究非绝热动态的最有前途的方法之一。该算法及其收敛属性也经过数值验证。此外，我们进行了数值实验，包括探索核动力学，电子传递和有限的温度效应，并证明我们的方法捕获了无法通过经典的表面跳跃轨迹捕获的物理学。
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.
