A great deal can be said about why MD is a useful simulation technique. Perhaps the most impor­tant statement is that, in this method, one follows the atomic motions according to the principles of classical mechanics as formulated by Newton and Hamilton. Because ofthis, the results are physically as meaningful as the potential U that is used. One does not have to apologize for any approximation in treating the N-body problem. Whatever mechanical, thermodynamic, and statistical mechanical properties that a system of N particles should have, they are all present in the simulation data. Of course, how one extracts these properties from the simulation output — the atomic trajectories — determines how useful the simulation is. We can regard MD simulation as an ‘atomic video’ of the particle motion (which can be displayed as a movie), and how to extract the information in a scien­tifically meaningful way is up to the viewer. It is to be expected that an experienced viewer can get much more useful information than an inexperienced one.

The above comments aside, we present here the general reasons why MD simulation is useful (or unique). These are meant to guide the thinking of the nonexperts and encourage them to discover and appreciate the many significant aspects of this simulation technique.

(a) Unified study of all physical properties. Using MD, one can obtain the thermodynamic, structural, mechanical, dynamic, and transport properties of a system of particles that can be studied in a solid, liquid, or gas. One can even study chemical properties and reactions that are more difficult and will require using quantum MD, or an empirical potential that explicitly models charge transfer.27

(b) Several hundred particles are sufficient to simulate bulk matter. Although this is not always true, it is rather surprising that one can get quite accurate ther­modynamic properties such as equation of state in this way. This is an example that the law of large numbers takes over quickly when one can average over several hundred degrees of freedom.

(c) Direct link between potential model and physical proper­ties. This is useful from the standpoint of funda­mental understanding of physical matter. It is also very relevant to the structure-property correla­tion paradigm in material science. This attribute has been noted in various general discussions of the usefulness of atomistic simulations in material research.28-30

(d) Complete control over input, initial and boundary conditions. This is what provides physical insight into the behavior of complex systems. This is also what makes simulation useful when com­bined with experiment and theory.

(e) Detailed atomic trajectories. This is what one obtains from MD, or other atomistic simulation tech­niques, that experiment often cannot provide. For example, it is possible to directly compute and observe diffusion mechanisms that otherwise may be only inferred indirectly from experi­ments. This point alone makes it compelling for the experimentalist to have access to simulation.

We should not leave this discussion without reminding ourselves that there are significant limitations to MD as well. The two most important ones are as follows:

(a) Need for sufficiently realistic interatomic potential functions U. This is a matter of what we really know fundamentally about the chemical binding of the system we want to study. Progress is being made in quantum and solid-state chemistry and condensed-matter physics; these advances will make MD more and more useful in understand­ing and predicting the properties and behavior of physical systems.

(a) Computational-capability constraints. No computers will ever be big enough and fast enough. On the other hand, things will keep on improving as far as we can tell. Current limits on how big and how long are a billion atoms and about a microsecond in brute force simulation. A billion-atom MD simulation is already at the micrometer length scale, in which direct experimental observations (such as transmission electron microscopy) are available. Hence, the major challenge in MD simulations is in the time scale, because most of the processes of interest and experimental obser­vations are at or longer than the time scale of a millisecond.