Modified Group Contribution Scheme to Predict the Glass-Transition Temperature of Homopolymers through a Limiting Property Dataset

[Image: see text] Previous studies on glass-transition temperature (T(g)) prediction mainly focus on developing diverse methods with higher regression accuracy, but very little attention has been paid to the dataset. Generally, a large range of T(g) values of a specified polymer could be found in the literature but which one should be selected into a dataset merely depends on the implicit preference rather than a recognized and clear criterion. In this paper, limiting glass-transition temperature (T(g)(∞)), a constant value obtained at the infinite number-average molecular weight M(n), was validated to be an adequate bridge index in the T(g) prediction models. Furthermore, a new dataset containing 198 polymers was established to predict T(g)(∞) using the improved group contribution method and it showed a good correlation (R(2) = 0.9925, adjusted R(2) = 0.9894). The method could also generate T(g)–M(n) curves by introducing the T(g)(∞) function and provide more information to polymer scientists and engineers for material selection, product design, and synthesis.