Multiscale quantum simulations using

This section describes the main methodological advances that will be used in subsequent selected applications, including (1) Development of fast semiempirical methods for multiscale quantum simulations, (2) Directions for development of next-generation QM/MM models, and (3) Linear-scaling electrostatic and generalized solvent boundary methods. 

In this section, we give a brief overview of theoretical methods used to perform tribological simulations. We restrict the discussion to methods that are based on an atomic-level description of the system. We begin by discussing generic models, such as the Prandtl-Tomlinson model. Below we explore the use of force fields in MD simulations. Then we discuss the use of quantum chemical methods in tribological simulations. Finally, we briefly discuss multiscale methods that incorporate multiple levels of theory into a single calculation. 

The main purpose of quantum-chemical modeling in materials simulation is to obtain necessary input data for the subsequent calculations of thermodynamic and kinetic parameters required for the next steps of multiscale techniques. Quantum-chemical calculations can also be used to predict various physical and chemical properties of the material in hand (the growing film in our case). Under quantum-chemical, we mean here both molecular and solid-state techniques, which are now implemented in numerous computer codes (such as Gaussian [25], GAMESS [26], or NWCHEM [27] for molecular applications and VASP [28], CASTEP [29], or ABINIT [30] for solid-state applications). 

A widely used type of multiscale simulation combines quantum mechanical and molecular mechanical (QM/MM) simulations. In this approach, the functional core of the molecular system, for example, the catalytic sites of an enzyme, is described at the electronic level (QM region), whereas the surrounding macromolecular system is treated using a classical description (MM region). Some of the biological applications for which QM/MM calculations have been widely utilized are chemical reactions in enzymes, proton transfer in proteins and optical excitations. In QM/MM... 

Abstract The goal of multiscale modeUing of heterogeneous catalytic reactors is the prediction of all steps, starting from the reaction mechanism at the active centre, the rates of reaction, adsorption and diffusion processes inside the porous system of the catalyst support, based on first principles, quantum chemistry, force field simulations and macroscopic differential equations. The progress in these fields of research will be presented, including linking models between the various levels of description. Alkylation of benzene will be used as an example to demonstrate the various approaches from the active centre to the reactor. 

Molecular simulation of heterogeneous catalytic reactors, which is a multiscale problem (Fig. 5) initiated by quantum chemical calculations, may be used in combination with TST to obtain intrinsic kinetic data and to elucidate the reactimi... 

Figure 6.1 The scheme for the bottom-up multiscale simulation method, in which the quantum chemistry method, including first-principle calculations and DFT, was used to obtain the binding energy between gas molecules and COF materials. By fitting the binding energy into the molecular force fields and further inputting the force fields into a statistical mechanics-based molecular simulation, we can predict adsorption properties of COF materials. This bottom-up multiscale method spans three scales, including the electronic scale, the molecular scale, and the macroscale.

This is one of the simple and most commonly used method to perform multiscale simulation. By definition calculation of parameters for classical MD simulation from quantum chemical calculation is also a multiscale simulation. Therefore, most of the force filed e.g., OPLS," AMBER, GROMOS available for simulations of liquid, polymers, biomolecules are derived from quantum chemical calculations can be termed as multiscale simulation. To bridge scales from classical MD to mesoscale, different parameter can be calculated and transferred to the mesoscale simulation. One of the key examples will be calculation of solubiUty parameter from all atomistic MD simulations and transferring it to mesoscale methods such dissipative particle dynamics (DPD) or Brownian dynamics (BD) simulation. Here, in this context of multiscale simulation only DPD simulation along with the procedure of calculation of solubility parameter from all atomistic MD simulation will be discussed. 

Multiscale simulation involves the use of distinct methods appropriate for different length and time scales that are applied simultaneously to achieve a comprehensive description of a system. Figure 4-4 illustrates some of the computational methods that have been developed over many decades in order to deal with phenomena at different time and length scales to compute properties and model phenomena. These include quantum mechanics for accurate calculation of small... 

It has been shown that such electron dynamics can be performed in good agreement with time-resolved experiments for perylene-Ti02 interfaces with different anchor and spacer groups. In particular, the use of electronic structure information from previous atomistic quantum chemical calculations provides an illustration of a successful combination of different levels of theory into a first principles-based multiscale approach. Theoretical developments of such electron dynamics simulations have recently also included vibrational effects that can be incorporated from separate calculations of molecular vibrational modes. ... 

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