This work explores a continuum representation for diffuse layer models, thereby endowing continuum embedding models the ability to capture electrostatic phenomena in the environment such as the existence of electrolyte ions, and the nature of ionic liquids. It introduces a new field-aware continuum model that adjusts the size of the quantum regime per atom based on the distribution of charge in a system. The model accounts for the asymmetric nature of solvent distribution when applied to cations versus anions; it also overcomes the need to parameterize continuum interface models for different charged systems. The continuum representation of cavitation in water does not account for the tendency for water to form a hydrogen bonding network that is broken due to the formation of cavities. This effect is a major contributor to hydrophobic solvation and is an important precondition to the investigation of solvated proteins with continuum embedding. A new model inspired by machine learning advances is trained on molecular dynamics simulations due to the difficulty of isolating the cavitation energy term in experiment. Thermodynamic integration is used to calculate the energy from a step-like repulsive potential from cavities in TIP4P water, cavities ranging from small organic molecules, to small proteins. Predictions from this new model show a small improvement for small molecules and scale much better with respect to the size of the system.