Availability of the general-purpose molecular simulation suites such as CHARMM, 816) AMBER, 817) GROMOS, 818) OPLS, 819) and later NAMD promoted the use of MD methods.
Atomistic-based simulations such as molecular mechanics, molecular dynamics, and Monte Carlo-based methods have come into wide use for material design.
Atomistic based simulations such as molecular mechanics (MM), molecular dynamics (MD), and Monte Carlo-based methods (MC) have come into wide use for material design.
Atomistic-based simulations such as molecular mechanics (MM), molecular dynamics (MD), and Monte Carlo-based methods (MC) have come into wide use for materials design.
For new systems, such properties may be unknown and hard to measure or estimate from numerical simulations such as molecular dynamics.
Also, many protein folding simulations such as molecular dynamics (MD) require large amounts of CPU time for protein structure folding and/or prediction and require templates (or homologs) for initiation [ 3].
Alternative popular approaches include endpoint methods that involve implicit solvent post-processing of explicit-solvent simulations, such as molecular mechanics with Poisson Boltzmann or generalized Born and surface area (MM/PBSA and MM/GBSA) methods.
Advanced theoretical procedures such as molecular simulations are needed to properly model the heterogeneous molecular distribution inside the shale nanopores.
There are numerous computationally demanding applications that can benefit from grid computing such as molecular simulations and large-scale optimizations.
