Design of Polyelectrolyte-based Materials using Molecular Modeling

Polyelectrolyte block copolymers have shown promise as carriers for drug and gene delivery due to their ability to self-assemble into a variety of responsive morphologies with tailored properties. Dissipative Particle Dynamics (DPD) is a computationally inexpensive, coarse-grained simulation technique that has previously been used to predict the resulting morphologies of polyelectrolyte block copolymers. However, the design space of these materials is still largely unexplored with the influence of most design parameters being poorly understood. This study utilizes DPD and machine learning methods to investigate a multi-dimensional, morphological phase diagram for polyelectrolyte block copolymers. A machine leaning method, support vector machine (SVM), is trained and tested on DPD simulation data to handle the high dimension of features influencing the polyelectrolyte block copolymer morphology. The results are the development of a comprehensive and detailed morphological phase diagrams for block copolymer properties versus environmental conditions that can be used as a robust predictive tool. These results provide the fundamental basis for synthesizing polyelectrolyte block copolymers for materials with novel, desirable properties.