Training Set Data

Given the training set, next task is to generate data for developing force field. Quantum mechanics calculations are powerful to produce large amount of information in terms of energy, energy derivatives, electrostatic potentials and electron densities, atomic partial charges, etc., which can be used to derive force field parameters. However, how to sample the molecular conformations is a critical issue. Only with sufficient sampling the conformational spaces, the force field derived can be expected to represent the conformers well.

Using the small fragments implies that long-range (nonbond) interactions are to be considered separately. In addition, the nonbond interactions cannot be represented well in QM calculations. Even with the electron correlations are considered, the polarization and multibody effects are very difficult to be captured by using QM calculations. A common practice is to use experimental data of molecular liquids to optimize the nonbond interaction parameters. Therefore, another training set of liquid molecules, which often overlaps with the training set of fragments, is required. The experimental data usually includes equilibrium data such as liquid density at given temperature and pressure, heat of vaporization energy, vapor-liquid-equilibrium curve, as well as dynamic data such as diffusion coefficients and viscosity.