Abstract
Force fields for four small molecules, methane, ethane, methanol, and ethanol, were created by force matching MP2 gradients computed with triple-zeta-quality basis sets using the Adaptive Force Matching method. Without fitting to any experimental properties, the force fields created were able to predict hydration free energies, enthalpies of hydration, and diffusion constants in excellent agreements with experiments. The root mean square error for the predicted hydration free energies is within 1 kJ/mol of experimental measurements of Ben-Naim et al. [J. Chem. Phys. 81(4), 2016–2027 (1984)]. The good prediction of hydration free energies is particularly noteworthy, as it is an important fundamental property. Similar hydration free energies of ethane relative to methane and of ethanol relative to methanol are attributed to a near cancellation of cavitation penalty and favorable contributions from dispersion and Coulombic interactions as a result of the additional methyl group.
Highlights
Computer simulations of molecular systems are routinely utilized in many scientific disciplines
The ability of these force fields (FFs) to predict hydration free energies (HFEs) is of particular interest due to the HFE being an important fundamental property
These solutes were chosen partly because experimental HFEs are readily available by which to gauge the quality of the Adaptive Force Matching (AFM) potentials
Summary
Computer simulations of molecular systems are routinely utilized in many scientific disciplines. Since quantum mechanical (QM) simulations are computationally intensive for systems with thousands of atoms or more, molecular mechanics (MM) force fields (FFs) are an indispensable tool in molecular simulations. Of essential importance, especially for studying complex systems over extended time scales.. The development of some of the most popular FFs has relied on a mix of experimental and ab initio data. The Assisted Model Building with Energy Refinement (AMBER) and Optimized Potentials for Liquid Simulations, All-Atom (OPLS-AA) rely on experimental data for bonds and angles and use mostly QM data for dihedral parameters.. While AMBER and Chemistry at Harvard Molecular Mechanics (CHARMM) obtain partial charges based on QM, OPLS-AA and Groningen Molecular Simulation (GROMOS) The Assisted Model Building with Energy Refinement (AMBER) and Optimized Potentials for Liquid Simulations, All-Atom (OPLS-AA) rely on experimental data for bonds and angles and use mostly QM data for dihedral parameters. While AMBER and Chemistry at Harvard Molecular Mechanics (CHARMM) obtain partial charges based on QM, OPLS-AA and Groningen Molecular Simulation (GROMOS)
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