Machine Learning and Data in Polymer Physics Research - Interpretation of Experiments, Model Development, and Enhanced Sampling

Advances in molecular modeling algorithms, optimization strategies, and machine learning techniques, are ushering a new era of materials science and engineering in which computational tools are routinely used to probe, design, and interrogate matter and functional materials systems. The way in which problems and questions are formulated is rapidly changing, and it is important to rethink the role of research scientists and engineers in the context of these advances. In this presentation I will illustrate some of these ideas by relying on a variety of examples taken from polymer physics. In the first, I will discuss the coupling of experiments and molecular models, and how that coupling can be used to extract additional information from experiments that would otherwise be difficult to generate. In the second I will present models of biological systems – DNA and chromatin - that use machine learning to integrate experimental and computational information form a wide range of sources, and explain how the resulting information can be used to address important questions in epigenetics. In the third, I will discuss how machine learning can be used to design polymer structures or architectures for specific target properties.