首页> 外文期刊>The Journal of Chemical Physics >Mean field approximation for the stochastic Schrodinger equation
【24h】

Mean field approximation for the stochastic Schrodinger equation

机译:随机薛定inger方程的平均场近似

获取原文
获取原文并翻译 | 示例
           

摘要

A stochastic mean field (SMF) approach to nonadiabatic molecular simulations is introduced. Based on the quantum-classical mean-field approximation, SMF extents the classical model of the environment to incorporate its quantum properties. SMF differs from the ordinary mean-field method by the presence of additional terms in the Schrodinger equation that are due to the system-environment interaction. SMF resolves the two major drawbacks of mixed quantum-classical models. First, decoherence effects in the quantum subsystem are rigorously included. Present in all open systems, decoherence is crucial for nonadiabatic transitions taking place in condensed media. Second, the correct branching of the quantum-classical trajectories is achieved. In earlier approaches, the correct branching of the trajectories was attained via ad hoc surface hopping procedures, which experienced the hop rejection problem and could produce unfavorable classical trajectories in regions of nonadiabatic transitions depending on the quantum basis. It is shown that the correct branching of the trajectories is a direct consequence of decoherence. It is argued that the hop rejection problem disappears in SMF. The decoherence operator is discussed in detail, and the properties of the SMF method are illustrated with model simulations.
机译:介绍了一种用于非绝热分子模拟的随机平均场(SMF)方法。基于量子-经典平均场近似,SMF扩展了环境的经典模型以合并其量子特性。 SMF与普通平均场方法的区别在于Schrodinger方程中存在附加项,这是由于系统与环境之间的相互作用所致。 SMF解决了混合量子经典模型的两个主要缺点。首先,严格地包括了量子子系统中的退相干效应。存在于所有开放系统中,去相干对于在压缩媒体中发生的非绝热过渡至关重要。其次,实现了量子古典轨迹的正确分支。在较早的方法中,轨迹的正确分支是通过特殊的表面跳频程序实现的,该程序经历了跳频抑制问题,并可能根据量子基础在非绝热跃迁的区域中产生不利的经典轨迹。结果表明,轨迹的正确分支是退相干的直接结果。有人认为跳频抑制问题在SMF中消失了。对退相干算子进行了详细讨论,并通过模型仿真说明了SMF方法的性质。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号