(713a) Polymer Design in the Era of Machine Learning

Advances in molecular modeling algorithms, optimization strategies, and machine learning techniques, are ushering a new era of polymer science and engineering in which computational tools could routinely be used to probe, design, and interrogate polymeric materials systems. The way in which problems and questions are formulated is rapidly changing, and it is important to rethink the role of scientists and engineers in the context of these advances. In this presentation I will illustrate some of these ideas by relying on several examples taken from our own research. In the first, I will discuss the automated creation of data bases, and the development of new graph based neural network strategies to represent polymeric structures. I will also discuss how such a framework can then be used to predict the properties of polymers and to design new materials with target properties. In the second, I will discuss how multiscale modeling and machine learning can be used to engineer the structure and, ultimately, the rheology of polymeric materials. In a third example, I will discuss how results from new molecular models and advanced sampling algorithms and data from multiple experiments can be coupled and reconciled by relying on machine learning and image analysis methods to decipher the structure of chromatin – chromosome length DNA that is tightly packed in the cell nucleus.