Institut für Physik, Johannes Gutenberg-Universität Mainz, Staudingerweg 9, 55128 Mainz, Germany.
Soft Matter. 2018 Nov 28;14(46):9368-9382. doi: 10.1039/c8sm01817k.
We propose a generalized Langevin dynamics (GLD) technique to construct non-Markovian particle-based coarse-grained models from fine-grained reference simulations and to efficiently integrate them. The proposed GLD model has the form of a discretized generalized Langevin equation with distance-dependent two-particle contributions to the self- and pair-memory kernels. The memory kernels are iteratively reconstructed from the dynamical correlation functions of an underlying fine-grained system. We develop a simulation algorithm for this class of non-Markovian models that scales linearly with the number of coarse-grained particles. Our GLD method is suitable for coarse-grained studies of systems with incomplete time scale separation, as is often encountered, e.g., in soft matter systems. We apply the method to a suspension of nanocolloids with frequency-dependent hydrodynamic interactions. We show that the results from GLD simulations perfectly reproduce the dynamics of the underlying fine-grained system. The effective speedup of these simulations amounts to a factor of about 104. Additionally, the transferability of the coarse-grained model with respect to changes of the nanocolloid density is investigated. The results indicate that the model is transferable to systems with nanocolloid densities that differ by up to one order of magnitude from the density of the reference system.
我们提出了一种广义朗之万动力学（GLD）技术，用于从细粒度参考模拟构建非马尔可夫基于粒子的粗粒模型，并对其进行有效整合。所提出的 GLD 模型具有离散广义朗之万方程的形式，其中距离相关的双粒子贡献自和对自记忆核。记忆核从基础细粒度系统的动态相关函数中迭代重建。我们开发了一种适用于此类非马尔可夫模型的模拟算法，其与粗粒度粒子的数量呈线性比例。我们的 GLD 方法适用于不完全时间尺度分离的系统的粗粒研究，例如，在软物质系统中经常遇到这种情况。我们将该方法应用于具有频率相关流体动力学相互作用的纳米胶体悬浮液中。我们表明，GLD 模拟的结果完美地再现了基础细粒度系统的动力学。这些模拟的有效加速因子约为 104。此外，还研究了粗粒模型对纳米胶体密度变化的可转移性。结果表明，该模型可转移到纳米胶体密度与参考系统的密度相差一个数量级的系统。
