Molecular simulation

(chemistry) Computational techniques for predicting many useful functional properties of chemicals and materials, including thermodynamic properties, thermochemical properties, spectroscopic properties, mechanical properties, transport properties, and morphological information.

A tool for predicting entirely computationally many useful functional properties of systems of interest in the chemical, pharmaceutical, materials, and related industries. Included are thermodynamic, thermochemical, spectroscopic, mechanical, and transport properties, and morphological information (such as location and shape of binding sites on a biomolecule and crystal structure).

The two main molecular simulation techniques are molecular dynamics and Monte Carlo simulation, both of which are rooted in classical statistical mechanics. Given mathematical models for the internal structure of each molecule (the intramolecular potential which describes the energy of each conformation of the molecule) and the interaction between molecules (the intermolecular potential which describes the energy associated with molecules being in a particular conformation relative to each other), classical statistical mechanics provides a formalism for predicting properties of a macroscopic collection of such molecules based on statistically averaging over the possible microscopic states of the system as it evolves under the rules of classical mechanics. Thus, the building blocks are molecules, the dynamics are described by classical mechanics, and the key concept is statistical averaging. In molecular dynamics, the microscopic states of the system are generated by solving the classical equations of motion as a function of time (typically over a period limited to tens of nanoseconds). Thus, one can observe the relaxation of a system to equilibrium (provided the time for the relaxation falls within the time accessible to molecular dynamics simulation), and so molecular dynamics permits the calculation of transport properties which at the macroscopic scale describe the relaxation of a system in response to inhomogeneities. In Monte Carlo simulation, equilibrium configurations of systems are generated stochastically according to the probabilities rigorously known from classical statistical mechanics. Thus, Monte Carlo simulation generates equilibrium states directly (which has many advantages, including bypassing configurations which are not characteristic of equilibrium but which may be difficult to escape dynamically) and so can be used to study equilibrium configurations of systems which may be expensive or impossible to access via molecular dynamics. The drawback of Monte Carlo simulation is that it cannot yield the kind of dynamical response information that leads directly to transport properties. See also Chemical dynamics; Computational chemistry.

Computational quantum chemistry and molecular simulation methods can be used to predict properties that once were only accessible experimentally, resulting in several significant applications in basic and industrial research. These applications include providing estimates of properties for systems for which little or no experimental data are available, which is especially useful in the early stages of chemical process design; yielding insight into the molecular basis for the behavior of particular systems, which is very useful in developing engineering correlations, design rules, or quantitative structure-property relations; and providing guidance for experimental studies by identifying the interesting systems or properties to be measured.