Rinderspacher Berend Christopher, Bardhan Jaydeep P, Ismail Ahmed E
Weapons and Materials Directorate, United States Army Research Laboratory, Adelphi, Maryland 20783-1138, USA.
Department of Mechanical and Industrial Engineering, Northeastern University, Boston, Massachusetts 02115, USA.
Phys Rev E. 2017 Jul;96(1-1):013301. doi: 10.1103/PhysRevE.96.013301. Epub 2017 Jul 5.
We present a multiresolution approach to compressing the degrees of freedom and potentials associated with molecular dynamics, such as the bond potentials. The approach suggests a systematic way to accelerate large-scale molecular simulations with more than two levels of coarse graining, particularly applications of polymeric materials. In particular, we derive explicit models for (arbitrarily large) linear (homo)polymers and iterative methods to compute large-scale wavelet decompositions from fragment solutions. This approach does not require explicit preparation of atomistic-to-coarse-grained mappings, but instead uses the theory of diffusion wavelets for graph Laplacians to develop system-specific mappings. Our methodology leads to a hierarchy of system-specific coarse-grained degrees of freedom that provides a conceptually clear and mathematically rigorous framework for modeling chemical systems at relevant model scales. The approach is capable of automatically generating as many coarse-grained model scales as necessary, that is, to go beyond the two scales in conventional coarse-grained strategies; furthermore, the wavelet-based coarse-grained models explicitly link time and length scales. Furthermore, a straightforward method for the reintroduction of omitted degrees of freedom is presented, which plays a major role in maintaining model fidelity in long-time simulations and in capturing emergent behaviors.
我们提出了一种多分辨率方法，用于压缩与分子动力学相关的自由度和势，例如键势。该方法提出了一种系统的方式来加速具有两级以上粗粒度的大规模分子模拟，特别是聚合物材料的应用。具体而言，我们推导了（任意大的）线性（均）聚合物的显式模型以及从片段解计算大规模小波分解的迭代方法。这种方法不需要显式准备原子到粗粒度的映射，而是使用图拉普拉斯算子的扩散小波理论来开发特定于系统的映射。我们的方法导致了特定于系统的粗粒度自由度层次结构，为在相关模型尺度上对化学系统进行建模提供了一个概念清晰且数学严谨的框架。该方法能够根据需要自动生成任意数量的粗粒度模型尺度，即超越传统粗粒度策略中的两个尺度；此外，基于小波的粗粒度模型明确地将时间和长度尺度联系起来。此外，还提出了一种重新引入省略自由度的直接方法，该方法在长时间模拟中保持模型保真度以及捕捉涌现行为方面起着重要作用。
