Oil and water do not mix—hydrophobic hydration

Many surfactants contain hydrocarbon moieties that are removed from their aqueous environment (‘dehydrated’) in, for example, adsorption and micelle formation. Hydrophobic hydration relates to the interactions between individual nonpolar solute molecules and water, and can be probed using thermodynamic quantities for the dissolution of dilute hydrocarbon vapours to form dilute aqueous solutions. Contrary to the simple expectation that the entropy of hydration of a nonpolar moiety should be positive (due to disruption of water structure), it is large and negative, giving a large positive contribution to the free energy of hydration. The hydration of nonpolar molecules in water leads to an attraction between the molecules in close proximity, which is termed hydrophobic bonding. Although the free energy of hydration of nonpolar groups in bulk aqueous solution is positive, the interaction free energy of nonpolar molecules/groups with interfacial water at an air/water interface is negative.

QUBEKit: Automating the Derivation of Force Field Parameters from Quantum Mechanics

<div><div><div><p>Modern molecular mechanics force fields are widely used for modelling the dynamics and interactions of small organic molecules using libraries of transferable force field parameters. For molecules outside the training set, parameters may be missing or inaccurate, and in these cases, it may be preferable to derive molecule-specific parameters. Here we present an intuitive parameter derivation toolkit, QUBEKit (QUantum mechanical BEspoke Kit), which enables the automated generation of system-specific small molecule force field parameters directly from quantum mechanics. QUBEKit is written in python and combines the latest QM parameter derivation methodologies with a novel method for deriving the positions and charges of off-center virtual sites. As a proof of concept, we have re-derived a complete set of parameters for 109 small organic molecules, and assessed the accuracy by comparing computed liquid properties with experiment. QUBEKit gives highly competitive results when compared to standard transferable force fields, with mean unsigned errors of 0.024 g/cm3, 0.79 kcal/mol and 1.17 kcal/mol for the liquid density, heat of vaporization and free energy of hydration respectively. This indicates that the derived parameters are suitable for molecular modelling applications, including computer-aided drug design.</p></div></div></div>

QUBEKit: Automating the Derivation of Force Field Parameters from Quantum Mechanics

<div><div><div><p>Modern molecular mechanics force fields are widely used for modelling the dynamics and interactions of small organic molecules using libraries of transferable force field parameters. For molecules outside the training set, parameters may be missing or inaccurate, and in these cases, it may be preferable to derive molecule-specific parameters. Here we present an intuitive parameter derivation toolkit, QUBEKit (QUantum mechanical BEspoke Kit), which enables the automated generation of system-specific small molecule force field parameters directly from quantum mechanics. QUBEKit is written in python and combines the latest QM parameter derivation methodologies with a novel method for deriving the positions and charges of off-center virtual sites. As a proof of concept, we have re-derived a complete set of parameters for 109 small organic molecules, and assessed the accuracy by comparing computed liquid properties with experiment. QUBEKit gives highly competitive results when compared to standard transferable force fields, with mean unsigned errors of 0.024 g/cm3, 0.79 kcal/mol and 1.17 kcal/mol for the liquid density, heat of vaporization and free energy of hydration respectively. This indicates that the derived parameters are suitable for molecular modelling applications, including computer-aided drug design.</p></div></div></div>

QUBEKit: Automating the Derivation of Force Field Parameters from Quantum Mechanics

<div><div><div><p>Modern molecular mechanics force fields are widely used for modelling the dynamics and interactions of small organic molecules using libraries of transferable force field parameters. For molecules outside the training set, parameters may be missing or inaccurate, and in these cases, it may be preferable to derive molecule-specific parameters. Here we present an intuitive parameter derivation toolkit, QUBEKit (QUantum mechanical BEspoke Kit), which enables the automated generation of system-specific small molecule force field parameters directly from quantum mechanics. QUBEKit is written in python and combines the latest QM parameter derivation methodologies with a novel method for deriving the positions and charges of off-center virtual sites. As a proof of concept, we have re-derived a complete set of parameters for 109 small organic molecules, and assessed the accuracy by comparing computed liquid properties with experiment. QUBEKit gives highly competitive results when compared to standard transferable force fields, with mean unsigned errors of 0.024 g/cm3, 0.79 kcal/mol and 1.17 kcal/mol for the liquid density, heat of vaporization and free energy of hydration respectively. This indicates that the derived parameters are suitable for molecular modelling applications, including computer-aided drug design.</p></div></div></div>

A non-Bornian analysis of the Gibbs energy of hydration for organic ions

The hydration energy of organic ions can be well evaluated from the distribution of surface field strength, by using a simple semi-empirical equation.

Diffusion Modeling in Compacted Bentonite Based on Modified Gouy-Chapman Model

ABSTRACTThe integrated sorption and diffusion (ISD) model has been developed to quantify radionuclide transport in compacted bentonite. The current ISD model, based on averaged pore aperture and the Gouy-Chapman electric double layer (EDL) theory can quantitatively account for diffusion of monovalent cations and anions under a wide range of conditions (e.g., salinity, bentonite density). To improve the applicability of the current ISD model for multivalent ions and complex species, the excluded volume effect and the dielectric saturation effect were incorporated into the current model, and the modified Poisson-Boltzmann equations were numerically solved. These modified models had little effect on the calculation of effective diffusivity of Sr2+/Cs+/I−. On the other hand, the model, modified considering the effective electric charge of hydrated ions, calculated using the Gibbs free energy of hydration, agreed well with the diffusion data including those of Sr2+.