Methodology of Parameterization of Molecular Mechanics Force Field From   Quantum Chemistry Calculations using Genetic Algorithm: A case study of   methanol
  In molecular dynamics (MD) simulation, force field determines the capability of an individual model in capturing physical and chemistry properties. The method for generating proper parameters of the force field form is the key component for computational research in chemistry, biochemistry, and condensed-phase physics. Our study showed that the feasibility to predict experimental condensed phase properties (i.e., density and heat of vaporization) of methanol through problem specific force field from only quantum chemistry information. To acquire the satisfying parameter sets of the force field, the genetic algorithm (GA) is the main optimization method. For electrostatic potential energy, we optimized both the electrostatic parameters of methanol using the GA method, which leads to low deviations of between the quantum mechanics (QM) calculations and the GA optimized parameters. We optimized the van der Waals (vdW) parameters both using GA and guided GA methods by calibrating interaction energy of various methanol homo-clusters, such as nonamers, undecamers, or tridecamers. Excellent agreement between the training dataset from QM calculations (i.e., MP2) and GA optimized parameters can be achieved. However, only the guided GA method, which eliminates the overestimation of interaction energy from MP2 calculations in the optimization process, provides proper vdW parameters for MD simulation to get the condensed phase properties (i.e., density and heat of vaporization) of methanol. Throughout the whole optimization process, the experimental value were not involved in the objective functions, but were only used for the purpose of justifying models (i.e., nonamers, undecamers, or tridecamers) and validating methods (i.e., GA or guided GA). Our method shows the possibility of developing descriptive polarizable force field using only QM calculations. 
Ying Li, Hui Li, Maria K. Y. Chan, Subramanian Sankaranarayanan and   Beno\^it Rouxb
physics.atm-clus physics.chem-ph q-bio.QM