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源于经典轨迹的量子动力学。

Quantum Dynamics from Classical Trajectories.

作者信息

S Mattos Rafael, Mukherjee Saikat, Barbatti Mario

机构信息

Aix Marseille University, CNRS, ICR, 13397 Marseille, France.

Faculty of Chemistry, Nicolaus Copernicus University in Torun, 87100 Torun, Poland.

出版信息

J Chem Theory Comput. 2024 Sep 5. doi: 10.1021/acs.jctc.4c00783.

Abstract

Nonadiabatic molecular dynamics plays an essential role in exploring the time evolution of molecular systems. Various methods have been developed for this study, with varying accuracy and computational cost. One very successful among them is trajectory surface hopping, which propagates nuclei as classical trajectories using forces from a quantum description of the electrons and incorporates nonadiabatic effects through stochastic state changes during each trajectory propagation. A statistical analysis of an ensemble of the independent trajectories recovers the simulated system's behavior. This approach can give good results, but it is known to overlook nuclear quantum effects, leading to inaccurate predictions. Here, we present quantum dynamics from classical trajectories (QDCT), a new protocol to recover the quantum wavepacket from the classical trajectories generated by surface hopping. In this first QDCT implementation, we apply it to recover results at the multiple spawning level from postprocessing surface hopping precomputed trajectories. With a series of examples, we demonstrate QDCT's potential to improve the accuracy of the dynamics, correct decoherence effects, and diagnose problems or increase confidence in surface hopping results. All that comes at virtually no computational cost since no new electronic calculation is required.

摘要

非绝热分子动力学在探索分子系统的时间演化过程中起着至关重要的作用。针对该研究已开发出各种方法,其准确性和计算成本各不相同。其中一种非常成功的方法是轨迹表面跳跃,它利用电子的量子描述所产生的力将原子核作为经典轨迹进行传播,并通过每次轨迹传播过程中的随机状态变化纳入非绝热效应。对一组独立轨迹进行统计分析可恢复模拟系统的行为。这种方法能给出不错的结果,但众所周知它会忽略核量子效应,从而导致预测不准确。在此,我们提出了基于经典轨迹的量子动力学(QDCT),这是一种从表面跳跃产生的经典轨迹中恢复量子波包的新方案。在首次实施 QDCT 时,我们将其应用于通过后处理表面跳跃预先计算的轨迹来恢复多产卵水平的结果。通过一系列示例,我们展示了 QDCT 在提高动力学准确性、校正退相干效应以及诊断问题或增强对表面跳跃结果的信心方面的潜力。所有这些几乎无需计算成本,因为无需进行新的电子计算。

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