The predictive accuracies of the models were evaluated based on the root-mean-square error (RMSE) metric, which measures the difference between the actual critical concentration of the spin coated samples and the predicted critical concentration from the models. The RMSE results of all 10 models can be found in Table . The Curve-Fit model achieves the lowest RMSE of 1.37, which equates to a Mean Absolute Percentage Error (MAPE) of 6.55%, making it the best performing model for predicting one variable based on the other two input variables. Figure findings, which determined that an increase in molecular weight corresponded to a monotonic increase in thin film thickness. This increase is due to the increase in viscosity that occurs as molecular weight increases, which causes the thickness of the thin films to increase . In this way, our three-dimensional model is supported by the previously developed spin coating theory.