  Glass transition temperature ($T_{\text{g}}$) plays an important role in
controlling the mechanical and thermal properties of a polymer. Polyimides are
an important category of polymers with wide applications because of their
superior heat resistance and mechanical strength. The capability of predicting
$T_{\text{g}}$ for a polyimide $a~priori$ is therefore highly desirable in
order to expedite the design and discovery of new polyimide polymers with
targeted properties and applications. Here we explore three different
approaches to either compute $T_{\text{g}}$ for a polyimide via all-atom
molecular dynamics (MD) simulations or predict $T_{\text{g}}$ via a
mathematical model generated by using machine-learning algorithms to analyze
existing data collected from literature. Our simulations reveal that
$T_{\text{g}}$ can be determined from examining the diffusion coefficient of
simple gas molecules in a polyimide as a function of temperature and the
results are comparable to those derived from data on polymer density versus
temperature and actually closer to the available experimental data.
Furthermore, the predictive model of $T_{\text{g}}$ derived with
machine-learning algorithms can be used to estimate $T_{\text{g}}$ successfully
within an uncertainty of about 20 degrees, even for polyimides yet to be
synthesized experimentally.
