- © The Mineralogical Society Of America
A variety of classical (Newtonian) computational approaches have been developed to address the atomistic structure and behavior of materials, such as molecular dynamics (MD), monte carlo (MC), and molecular mechanics (MM) computer simulations. For the purposes of the current review, the discussion will be limited to the MD simulation technique. A major advantage of the classical MD technique is the ability to study large numbers of atoms, O(103–107), for relatively long times (from the standpoint of certain molecular events), O(ns-μs). By gathering data over these large numbers and times, the simulated time and number averages can be compared to the experimentally obtained time and number averages that are inherent in most experiments. If the simulations reproduce the experimental data (with some expected level of accuracy), the simulations offer the advantage of enabling an evaluation of the discrete events that caused the time and number averages. That is, because all of the atoms are labeled in the simulations, the exact atomistic behavior that cause the averaged data can be followed in order to determine the molecular mechanisms.
Although algorithms have been developed that employ some level of electronic structure calculations with MD or MC schemes, system sizes are still fairly limited because of computational complexity.
This chapter will discuss some aspects of the MD technique used in our laboratory to study glasses, glass surfaces, and interfaces, and some specific results.
MOLECULAR DYNAMICS COMPUTER SIMULATION TECHNIQUE
The data presented in this review were generated using the classical molecular dynamics (MD) computer simulation technique. Classical MD simulations involve solving Newton’s equations of motion for a system of interacting particles (atoms, ions, or hard spheres). The earliest MD simulations involved hard spheres (Alder and Wainwright 1957, 1959), but have been followed by decades of research into new algorithms and new interatomic potentials of varying complexity, …