Because of these non-linear relationships, we model all of functions to have a second-degree polynomial relationship between thickness and concentration, generally yielding much higher coefficient of determination values compared to traditional linear fits. This conclusion of a nonlinear relationship was also reached by Danglad-Flores et al. , justifying our modeling of thickness vs concentration as a polynomial function. It can be observed in Table To determine the relationship between coverage thickness of monodisperse PS coatings and their bulk molecular weight, the same average thickness values were plotted against molecular weight for the five different concentration concentrations. As depicted in Figure , in every initial solution concentration, the average thickness of the film coatings increases monotonically as molecular weight of the bulk PS increases. This monotonic increase in film thickness is accounted for in previous studies in spin coating theory, which determined that higher molecular weight PS samples result in higher viscosity of the coating solution . Hall et al. presented a cube root relationship between thickness and molecular weight. Therefore, the equation that fits for thickness vs molecular weight data in Figure The corresponding regression coefficients for this equation are listed in Table . The previously presented two-dimensional graphs are "slices" of the three-dimensional manifold, as they embody the individual relationships that the machine learning model aggregates to synthesize 3D models, the process of which will be discussed in the following section.