Use of Three-Dimensional Molecular Descriptors to Predict the Glass Transition Temperature of Polymers

In the present study, we built several models based on three-dimensional molecular descriptors to predict the glass transition temperature using a data set of 117 polymers. A data set division was established (training and test data) and consequently the models were developed and validated. Finally, the prediction/screen of the top models were compared. Three main descriptors were obtained with excellent predictions: E2 (E2u and E2s), which encodes angular and radial information about atomic configuration, usually in relation to two atoms; TDB (TDB10u, TDB10e, TDB10s) describes the relationship between the average three-dimensional (Euclidean) distance and the topological distance (path length, or number of bonds) between possible atom pairs in a molecule; and RDF (RDF25i, RDF65u, RDF25u) describes the density of atoms at different distances from a reference atom, capturing information about the local structure of the molecule. An initial exploratory GA-LDA classification analysis using 3D descriptors revealed only partial discrimination between polymers with distinct Tg behavior, indicating that simplified 3D structural representations alone are limited for robust Tg prediction. Consequently, graph-based (2D) descriptors models were created and the prediction of the Tg was successfully achieved. Overall, the most influential variables are predominantly graph-based (2D) descriptors associated with molecular connectivity patterns (e.g., autocorrelation-type descriptors such as ATS2*), topological/shape-related indices (TSC* family), and ring-related terms. This attribution profile is consistent with the expected physicochemical determinants of the glass transition: polymer repeat units with higher structural rigidity, constrained connectivity, and increased ring/unsaturation content that typically exhibits reduced segmental mobility and, therefore, higher Tg.