Elucidating the Physicochemical Basis of the Glass Transition Temperature in Linear Polyurethane Elastomers with Machine Learning

The glass transition
temperature (Tg) is a fundamental property
of polymers that strongly influences
both mechanical and flow characteristics of the material. In many
important polymers, configurational entropy of side chains is a dominant
factor determining it. In contrast, the thermal transition in polyurethanes
is thought to be determined by a combination of steric and electronic
factors from the dispersed hard segments within the soft segment medium.
Here, we present a machine learning model for the Tg in linear polyurethanes and aim to uncover the underlying
physicochemical parameters that determine this. The model was trained
on literature data from 43 industrially relevant combinations of polyols
and isocyanates using descriptors derived from quantum chemistry,
cheminformatics, and solution thermodynamics forming the feature space.
Random forest and regularized regression were then compared to build
a sparse linear model from six descriptors. Consistent with empirical
understanding of polyurethane chemistry, this study indicates the
characteristics of isocyanate monomers strongly determine the increase
in Tg. Accurate predictions of Tg from the model are demonstrated, and the significance
of the features is discussed. The results suggest that the tools of
machine learning can provide both physical insights as well as accurate
predictions of complex material properties.