Methodology of Parameterization of Molecular Mechanics Force Field From Quantum Chemistry Calculations using Genetic Algorithm: A case study of methanol
Ying Li, Hui Li, Maria K. Y. Chan, Subramanian Sankaranarayanan and, Beno\^it Rouxb

TL;DR
This study develops a method to parameterize molecular mechanics force fields for methanol using genetic algorithms based solely on quantum chemistry data, successfully predicting experimental condensed phase properties.
Contribution
It introduces a guided genetic algorithm approach for optimizing force field parameters from quantum calculations without relying on experimental data during optimization.
Findings
Guided GA yields accurate van der Waals parameters for MD simulations.
Optimized electrostatic parameters closely match quantum mechanical calculations.
Method successfully predicts experimental density and heat of vaporization of methanol.
Abstract
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…
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Taxonomy
TopicsProtein Structure and Dynamics · Spectroscopy and Quantum Chemical Studies · Advanced Chemical Physics Studies
