Determining glass transition in all-atom acrylic polymeric melt simulations using machine learning.

The functionality of many polymeric materials depends on their glass transition temperatures (Tg). In computer simulations, Tg is often calculated from the gradual change in macroscopic properties. Precise determination of this change depends on the fitting protocols. We previously proposed a robust data-driven approach to determine Tg from the molecular dynamics simulation data of a coarse-grained semiflexible polymer model. In contrast to the global macroscopic properties, our method relies on high-resolution microscopic details. Here, we demonstrate the generality of our approach by using various dimensionality reduction and clustering methods and apply it to an atomistic model of acrylic polymers. Our study reveals the explicit contribution of the side chain and backbone residues in influencing the determination of the glass transition temperature.