Publication Date


Date of Final Oral Examination (Defense)


Type of Culminating Activity


Degree Title

Doctor of Philosophy in Materials Science and Engineering


Materials Science and Engineering

Major Advisor

Eric Jankowski, Ph.D.


Elton Graugnard, Ph.D.


Lan Li, Ph.D.


Kevin Ausman, Ph.D.


This work aims to inform the formulation and processing of polymer mixtures through the use of models that have minimally sufficient complexity. Models with minimal complexity are easier to develop, understand, explain, and extend, all of which underpin model validation, verification, and reproducibility.

We develop simplified models for two different material systems, semiconducting polymers and thermosets. With the relatively low cost of predicting morphologies enabled by these models, we investigate structure-property-processing relationships in record system sizes and combinatorial parameter spaces. The insight from these models lays the foundation for improving the efficiency of organic solar cells and air travel.

The morphology of the active layer of an organic solar cell determines its efficiency, but is also the most difficult aspect to control during manufacturing. Morphology can in principle be controlled through the thermodynamic self-assembly of active layer components. We develop models of two semiconducting polymers. We find our predictions are validated by morphological and charge transport measurements from experiments and we provide guidance for optimizing conditions for self-assembly.

Thermoset polymers present a unique modeling challenge because their properties are sensitive to processing kinetics that are at odds with thermodynamic modeling frameworks. The primary source of this difficulty is bridging time (1×10−12s) and length scales (1×10−10m) of reaction dynamics with the time (1×102s) and length scales (1×10−6m) of morphology evolution. We implement a coarse-grained model of toughened thermosets where each amine, epoxy, and toughener mer is treated as a single simulation element. This simplification allows us to reach the time and length scales necessary to model the epoxy amine reaction and observe curing-driven morphology evolution. We simulate curing of (100 nm)3, million-particle volumes, which allows observation of experimentally-relevant volume evolution.

To practice behaviors necessary for computational research to be usable and reproduced by others, we make available all the models, initial configurations, submission scripts, analysis scripts, and simulation data associated with this work with an opensource, permissive license. We describe software development practices and design choices that make this possible and discuss opportunities for improvement in future computational materials research.