The manifold accepts molecular weight and thickness as inputs and predicts the critical concentration, the solution concentration required to spin coat a sample of a specific molecular weight into the desired thickness, as output. The spin coated samples were used as training data points to inform the development and training of the manifold. 10 of the most popular machine learning algorithms, as shown in Table , were implemented as regression models to learn the relationship between concentration, thickness, and molecular weight.