Within the field of computational chemistry, solvent models are a variety of methods to account for the behavior of solvated condensed phases. Solvent models allow simulations and calculations of reactions and processes which take place in solvated phases. These include biological, chemical and environmental processes. Such calculation can lead to predictions and improved understanding of the physical processes occurring. Such models have been extensively tested and reviewed in scientific literature. The various models have their own pros and cons. Implicit models are generally computationally efficient and can provide a reasonable description of the solvent behaviour, but fail to account for the local fluctuations in solvent density around a solute molecule. The density fluctuation behaviour is due to solvent ordering around a solute and is particularly prevalent when one is considering water as the solvent. 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 computaionally demanding and can fail to reproduce some experimental results, often due to certain fitting methods and parametrization. Hybrid methodologies are another option. These methods incorporate aspects of implicit and explicit aiming to minimse computational cost whist retaining at least some spatial resolution of the solvent. These methods can require more experience to use them correctly and often contain post calculation correction terms.