Solvent model
[1][2][3] Solvent models enable simulations and thermodynamic calculations applicable to reactions and processes which take place in solution.
Explicit models are often less computationally economical, but can provide a physical spatially resolved description of the solvent.
However, many of these explicit models are computationally demanding and can fail to reproduce some experimental results, often due to certain fitting methods and parametrization.
These methods incorporate aspects of implicit and explicit aiming to minimize computational cost while retaining at least some spatial resolution of the solvent.
Generally speaking, for implicit solvents, a calculation proceeds by encapsulating a solute in a tiled cavity (See the figure below).
Continuum models have widespread use, including use in force field methods and quantum chemical situations.
3rd term - quantum mechanical dispersion energy; can be approximated using an averaging procedure for the solvent charge distribution.
SMx models (where x is an alphanumeric label to show the version) are based on the generalized Born equation.
The SMD model solves the Poisson-Boltzmann equation analogously to PCM, but does so using a set of specifically parametrised radii which construct the cavity.
[10] This model uses the scaled conductor boundary condition, which is a fast and robust approximation to the exact dielectric equations and reduces the outlying charge errors as compared to PCM.
This is a more intuitively realistic picture in which there are direct, specific solvent interactions with a solute, in contrast to continuum models.
These models generally occur in the application of molecular mechanics (MM) and dynamics (MD) or Monte Carlo (MC) simulations, although some quantum chemical calculations do use solvent clusters.
These simulations often utilize molecular mechanics force fields which are generally empirical, parametrized functions which can efficiently calculate the properties and motions of large systems.
Prior criteria are defined to aid the algorithm in deciding whether to accept the newly perturbed system or not.
These models are commonly geometrically constrained with aspects of the geometry fixed such as the bond length or angles.
[14] Advancements around 2010 onwards in explicit solvent modelling see the use of a new generation of polarizable force fields, which are currently being created.
One such method is the Atomic Multipole Optimised Energetics for Biomolecular Applications (AMOEBA) force field.
[1] Other emerging polarizable forcefields which have been applied to condensed phase systems are; the Sum of Interactions between Fragments ab initio computed (SIBFA)[16] and the Quantum Chemical Topology Force Field (QCTFF).
One can imagine having a QM core treatment containing the solute and may be a small number of explicit solvent molecules.
The Reference Interaction Site Model (RISM) can be thought of being closer to implicit solvent representations.
[19] RISM, a classical statistical mechanics methodology, has it roots in the integral equation theory of liquids (IET).
[5] Within the MOZ equations a solvated system can be defined in 3D space by three spatial coordinates (r) and three angles (Θ).
It is a common approximation to assume spherical symmetry, allowing one to remove the orientational (angular) degrees of freedom.
There are several valid approximations, the first was the HyperNetted Chain (HNC), which sets the unknown terms in the closure relation to zero.
Although appearing crude the HNC has been generally quite successfully applied, although it shows slow convergence and divergent behaviour in some cases.
COSMO-RS is able to account for a major part of reorientation and strong directional interactions like hydrogen bonding within the first solvation shell.
Quantitative Structure–Activity Relationships (QSAR)/Quantitative Structure–Property Relationships (QSPR), whilst unable to directly model the physical process occurring in a condensed solvent phase, can provide useful predictions of solvent and solvation properties and activities; such as the solubility of a solute.
Generally, QSAR/QSPR methods require descriptors; these come in many different forms and are used to represent physical features and properties of a system of interest.
Typically the known data comes from experimental measurement, although there is no reason why similar methods can not be used to correlate descriptor(s) with theoretical or predicted values.
[30] More recently the rise of deep learning has provided many methods to generate embedded representations of molecules.
