AIPAR: ab initio parametrization of intermolecular potentials for computer simulations

Area of Science:

• Computational chemistry
• Molecular modeling
• Physical chemistry

Background:

• Developing accurate intermolecular potentials is crucial for molecular simulations.
• Existing empirical methods like OPLS require extensive parameterization.
• A need exists for automated, ab initio methods for generating reliable interaction potentials.

Purpose of the Study:

• To introduce and validate the ab initio parametrization (AIPAR) technique for generating intermolecular potentials.
• To assess the performance of AIPAR-derived parameters against empirical OPLS parameters.
• To demonstrate the robustness and transferability of AIPAR for various organic molecules and water.

Main Methods:

• Implementation of the automated AIPAR technique within the SJBR program.
• Application of AIPAR to organic molecules: methanol, acetone, methanethiol, imidazole, oxazole, and furan.
• Comparison of AIPAR parameters with empirical OPLS parameters, particularly the all-atoms model.
• Conducting Monte Carlo simulations for aqueous methanol solutions and methanol-water binary mixtures.

Main Results:

• AIPAR-derived parameters show good agreement with empirical OPLS parameters, especially when using the all-atoms OPLS model.
• Monte Carlo simulations using AIPAR parameters yield thermodynamic and geometric results comparable to OPLS simulations.
• Favorable comparison extends to both aqueous solutions and binary mixtures, highlighting AIPAR's accuracy.

Conclusions:

• The ab initio parametrization (AIPAR) technique provides an unambiguous, automated, and accurate method for determining intermolecular potentials.
• AIPAR parameters demonstrate high precision, robustness, and transferability for polar molecules interacting with water.
• This method offers a reliable alternative to empirical parameterization for molecular simulations.