Numerical simulation, clustering and prediction of multi-component polymer precipitation

TL;DR

This paper combines physics-based simulations with machine learning to efficiently classify and predict the morphology of multi-component polymer systems, reducing computational costs and enabling rapid material design.

Contribution

It introduces an integrated approach using modified Cahn-Hilliard models and ML clustering/prediction techniques for polymer morphology analysis.

Findings

ML techniques achieved ≥90% accuracy in morphology prediction

Dimensionality reduction methods effectively analyzed morphology clusters

Integration of simulations and ML reduces the number of required simulations

Abstract

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.