Leveraging Polymer Theory for Improved Machine Learning

Machine learning as applied to polymer science has recently shown immense progress---mostly in areas where there are existing large datasets or where datasets can be generated quickly. However, there are numerous interesting problems where the dataset sizes are too small or the need to understand the physics behind the machine learning prediction is essential. Here, we aim to tackle both of these problems by incorporating domain knowledge into machine learning models. Specifically, using a toy system of polymers in different solvent qualities, we compare several methods for incorporating theory into machine learning using a simple, imperfect but easily interpretable theory. We also explore the intersection these methods with different machine models including random forest and Gaussian process regression.