(740a) Computational Prediction and Evolutionary Design of Polymer Glass-Formation Behavior

In most polymers, the glass transition is one of the most important phenomena determining performance properties including mechanical response, processability, and transport behavior. For this reason, predicting and achieving rational control of the glass transition is a longstanding goal of polymer science. However, the vast range of timescales associated with glass formation, coupled with a lack of an agreed-upon theoretical description of the problem, have posed major challenges to achieving this goal. Here I describe a new approach to this problem, combining efficient molecular dynamics simulations, physics-based heuristics, machine learning, and evolutionary algorithms to predict and design the polymer glass transition.

The authors acknowledge the W. M. Keck Foundation for generous financial support of this research.