Murtola Teemu, Bunker Alex, Vattulainen Ilpo, Deserno Markus, Karttunen Mikko
Department of Applied Physics and Helsinki Institute of Physics, Helsinki University of Technology, Finland.
Phys Chem Chem Phys. 2009 Mar 28;11(12):1869-92. doi: 10.1039/b818051b. Epub 2009 Feb 25.
In this review, we focus on four current related issues in multiscale modeling of soft and biological matter. First, we discuss how to use structural information from detailed models (or experiments) to construct coarse-grained ones in a hierarchical and systematic way. This is discussed in the context of the so-called Henderson theorem and the inverse Monte Carlo method of Lyubartsev and Laaksonen. In the second part, we take a different look at coarse graining by analyzing conformations of molecules. This is done by the application of self-organizing maps, i.e., a neural network type approach. Such an approach can be used to guide the selection of the relevant degrees of freedom. Then, we discuss technical issues related to the popular dissipative particle dynamics (DPD) method. Importantly, the potentials derived using the inverse Monte Carlo method can be used together with the DPD thermostat. In the final part we focus on solvent-free modeling which offers a different route to coarse graining by integrating out the degrees of freedom associated with solvent.
在本综述中，我们聚焦于软物质和生物物质多尺度建模中当前四个相关问题。首先，我们讨论如何利用来自详细模型（或实验）的结构信息，以分层且系统的方式构建粗粒度模型。这在所谓的亨德森定理以及柳巴尔特塞夫和拉克松宁的逆蒙特卡罗方法的背景下进行讨论。在第二部分，我们通过分析分子构象以不同视角看待粗粒化。这通过应用自组织映射来实现，即一种神经网络类型的方法。这种方法可用于指导相关自由度的选择。然后，我们讨论与流行的耗散粒子动力学（DPD）方法相关的技术问题。重要的是，使用逆蒙特卡罗方法导出的势可与DPD恒温器一起使用。在最后一部分，我们聚焦于无溶剂建模，它通过消除与溶剂相关的自由度提供了一种不同的粗粒化途径。
