  Multi-component polymer systems are of interest in organic photovoltaic and
drug delivery applications, among others where diverse morphologies influence
performance. An improved understanding of morphology classification, driven by
composition-informed prediction tools, will aid polymer engineering practice.
We use a modified Cahn-Hilliard model to simulate polymer precipitation. Such
physics-based models require high-performance computations that prevent rapid
prototyping and iteration in engineering settings. To reduce the required
computational costs, we apply machine learning techniques for clustering and
consequent prediction of the simulated polymer blend images in conjunction with
simulations. Integrating ML and simulations in such a manner reduces the number
of simulations needed to map out the morphology of polymer blends as a function
of input parameters and also generates a data set which can be used by others
to this end. We explore dimensionality reduction, via principal component
analysis and autoencoder techniques, and analyse the resulting morphology
clusters. Supervised machine learning using Gaussian process classification was
subsequently used to predict morphology clusters according to species molar
fraction and interaction parameter inputs. Manual pattern clustering yielded
the best results, but machine learning techniques were able to predict the
morphology of polymer blends with $\geq$ 90 $\%$ accuracy.
