Langevin动力学的拟辛传播子设计

旋-积分算法：一种用于非绝热动力学旋映射表示的精确和辛传播器

The Spin-MInt Algorithm: an Accurate and Symplectic Propagator for the Spin-Mapping Representation of Nonadiabatic Dynamics

本文提出旋-积分算法，用于非绝热动力学的旋映射表示，证明其辛性质，并展示其在不同模型中的准确性与效率。

刚体动力学的朗之万恒温器

Langevin Thermostat for Rigid Body Dynamics

提出一种基于四元数表示和朗之万动力学的等温刚体模拟方法，结合平移自由度的朗之万或梯度动力学正确采样NVT分布，并设计了准辛二次数值积分器，在TIP4P水模型上测试了性能。

Efficient Langevin dynamics for "noisy" forces

Efficient Boltzmann-sampling using first-principles methods is challenging for extended systems due to the steep scaling of electronic structure methods with the system size. Stochastic approaches provide a gentler system-size dependency at the cost of introducing "noisy" forces, which serve to limit the efficiency of the sampling. In the first-order Langevin dynamics (FOLD), efficient sampling is achievable by combining a well-chosen preconditioning matrix S with a time-step-bias-mitigating propagator (Mazzola et al., Phys. Rev. Lett., 118, 015703 (2017)). However, when forces are noisy, S is…

经典分子动力学的多项式传播子

Polynomial propagators for classical molecular dynamics

本文提出基于切比雪夫和牛顿多项式展开的经典分子动力学传播子，具有可控的任意阶精度，长期稳定性可接受，计算复杂度与 velocity Verlet 相当，并支持扩展与并行化。

新的 Langevin 和梯度热浴用于刚体动力学

New Langevin and Gradient Thermostats for Rigid Body Dynamics

本文提出两种新热浴用于刚体动力学，通过四元数表示旋转，并设计了三种 Langevin 热浴和一种梯度热浴的数值积分器，分析了其精度和效率。

Stationary state distribution and efficiency analysis of the Langevin equation via real or virtual dynamics

Langevin dynamics has become a popular tool to simulate the Boltzmann equilibrium distribution. When the repartition of the Langevin equation involves the exact realization of the Ornstein-Uhlenbeck noise, in addition to the conventional density evolution, there exists another type of discrete evolution that may not correspond to a continuous, real dynamical counterpart. This virtual dynamics case is also able to produce the desired stationary distribution. Different types of repartition lead to different numerical schemes, of which the accuracy and efficiency are investigated through studying…