Multi-scale molecular modeling computational methods

At the same time computational methods based on the molecular mechanics, molecular dynamics (MD), Monte Carlo (MC) and ab initio methods evolved to the high level of predictive capability, accelerating development of new materials with designed properties. For the multicomponent and multiphase systems (e.g., nanocomposites, blends, composites with properties adjusted by incorporation of intercalants or compatibilizers) the most promising is the hierarchical multi-scale molecular modeling (MSMM). Thus, while such molecular simulations as MD or MC efficiently analyze the molecular structure at 0.1-10 nm, MSMM,... 

During the past few decades, various theoretical models have been developed to explain the physical properties and to find key parameters for the prediction of the system behaviors. Recent technological trends focus toward integration of subsystem models in various scales, which entails examining the nanophysical properties, subsystem size, and scale-specified numerical analysis methods on system level performance. Multi-scale modeling components including quantum mechanical (i.e., density functional theory (DFT) and ab initio simulation), atom-istic/molecular (i.e., Monte Carlo (MC) and molecular dynamics (MD)), mesoscopic (i.e., dissipative particle dynamics (DPD) and lattice Boltzmann method (LBM)), and macroscopic (i.e., LBM, computational... 

Quantum mechanics is the bedrock upon which multi-scale models are built. For decades, it has been a source of parameters for force-field models, which are vastly less computationally expensive and hence able to reach much longer length and time scales. It is also being increasingly used in concert with force-field methods through mixed quantum mechanics/molecular mechanics... 

Multi-scale modeling provides a hierarchical computational approach to describe macroscopic catalytic processes. In this approach, atomistic methods (first principle quantum chemistry calculations and classical molecular dynamics) are used which reveal microscopic insight into the meehanisms and molecular-scale dynamics of reactions at electrode surfaces (for e.g. see [15,... 

Intensive studies in the area of dendritic macromolecules, which include applied research and are generally interdisciplinary, have created a need for a more systematic approach to dendritic architectures development that employs a multi-scale modeling and simulation approach. A possible way is to determine the atomic-scale characteristics of dendritic molecules using computer simulation and computational approaches. Computer simulation, as a powerful and modem tool for solving scientific problems, can be performed for dendritic architectures without synthesizing them. Computer simulation not only used to reproduce experiment to elucidate the invisible microscopic details and further explain experiments, but also can be used as a useful predictive tool. Currently, Monte Carlo, Brownian dynamics and molecular dynamics are the most widely used simulation methods for molecular systems [5]. 

Spatial multi-scale methods are based on the paradigm that in many real situations the atomic description is only required within small parts of the simulation domain whereas for the majority the continuum model is still valid. This allows one to apply concurrent continuum and molecular simulations for the respective parts of the simulation domain using a coupling scheme that permits to connect between the two domains. The majority of the spatial domain is calculated by continuum solvers (computational fluid dynamics) which are very fast and only the active part is calculated using molecular simulation methods. In some cases several other coarser-grained (mesoscale) methods than the atomic simulations ones are used as interfaces between the molecular simulation and the continuum domains. Such approaches are called hybrid molecular-continuum methods and allow the simulation of problems that are not accessible either by continuum or by pure molecular simulation methods. 

Particle methods (Molecular Dynamics, Dissipative Particle Dynamics, Multi-Particle Collision Dynamics) simulate a system of interacting mass points, and therefore thermal fluctuations are always present. The particles may have size and structure or they may be just point particles. In the former case, the finite solvent size results in an additional potential of mean force between the beads. The solvent structure extends over unphysically large length scales, because the proper separation of scale between solute and solvent is not computationally realizable. In dynamic simulations of systems in thermal equilibrium [43], solvent structure requires that the system be equilibrated with the solvent in place, whereas for a structureless solvent the solute system can be equilibrated by itself, with substantial computational savings [43]. Finally, lattice models have a (rigorously) known solvent viscosity, whereas for particle methods the existing analytical expressions are only approximations (which however usually work quite well). 

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