Silicon molecular dynamics calculation

P. Ballone, W. Andreoni, R. Car and M. Parrinello, Equilibrium Structures and Finite Temperature Properties of Silicon Microclusters from Ab Initio Molecular-Dynamics Calculations, Phys. Rev. Lett. 60... 

This approach was successfully used in modeling the CVD of silicon nitride (Si3N4) films [18, 19, 22, 23]. Alternatively, molecular dynamics (MD) simulations can be used instead of or in combination with the MC approach to simulate kinetic steps of film evolution during the growth process (see, for example, a study of Zr02 deposition on the Si(100) surface [24]). Finally, the results of these simulations (overall reaction constants and film characteristics) can be used in the subsequent reactor modeling and the detailed calculations of film structure and properties, including defects and impurities. 

The thickness of the adsorption layer was estimated from the fraction of adsorbed chain units measured by means of h Ty, Tj and H relaxation studies [7, 8, 10, 12]. From the known value of the specific surface of Aerosil, its volume fraction in mixtures and the fraction of low mobile chain units at the Aerosil surface, the thickness of the adsorption layer is estimated assuming imiform coverage of the filler particles by a PDMS layer of constant thickness. This calculation leads to a value of about 0.8 run [7]. This value is increased by a factor 1.5-2, if a part of the filler surface will not be accessible for PDMS chains due to direct contacts between the primarily filler particles in aggregates [27]. Thus, the chain adsorption causes a significant restriction of local motions only in one or two monolayers adjacent to the filler surface. A similar estimation of the adsorption layer thickness has been obtained by other methods such as, e.g. dielectric experiment [27], adsorption study [3], the viscosity of the boundary layer for silicon liquids at the surface of a glass [5], molecular dynamics simulations [6], and C NMR relaxation experiments [22]. 

Molecular dynamics and minimization of proteins locally enhanced sampling and free energy calculations along reaction paths by perturbation or thermodynamic integration. Stardent, Silicon Graphics, IBM, and HP workstations, moil-view for visualization of shaded spheres and sticks on Silicon Graphics. Available by anonymous ftp from 128.248.186.70. 

Gomes, C.J., M. Madrid, and C.H. Amon. Parallel Molecular Dynamics Code Validation Through Bulk Silicon Thermal Conductivity Calculations, in Proceedings of the 2003 ASME International Mechanical Engineering Congress and Exposition, IMECE 2003-42352. 2003. Washington, DC. Lee, Y.H., R. Biswas, C.M. Soukoulis, C.Z. Wang, C.T. Chan, and K.M. Ho, Molecular-Dynamics Simulation of Thermal Conductivity in Amorphous Silicon. Physical Review B, 1991.43(8) p. 6573-6580. 

Two important studies have predicted the melting point of sohd materials from first-principles calculations. Sugino and Car (1995) used Car-Parrinello molecular dynamics to estimate the melting point of silicon from first... 

Figure 12.7 Compiiier. sinnilaiions of coalescence times for particles composed of varying numbers of silicon atoms. The dotted lines connecuhe molecular dynamic compulations based on the approach of the moment of inertia of two coalescing particles in contact to the value for the sphere of (he same volume. The solid lines show calculations ba.scd on the phenomenological theories, (12.3) and (12.6). Values calculated from (12.6) (for the liquid) were multiplied by a factor of 10 on the grounds that viscosity values fur bull silicon are (oo smalt for nanoparticles. (After Zachariah and Carrier. 1099.)...

As a test system, we used clusters of 29-71 silicon atoms. The configurations were generated by molecular dynamics simulation at 3,000 K, where the forces were obtained from the StiUinger—Weber potential [4]. We performed the electronic structure calculations of the different clusters using ONETEP, and we also used ONETEP to determine the atomic energies, as described in Sect. 6.1.2. A typical cluster is shown in Fig. 6.1. 

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