However, the correlation between actual errors and uncertainties is proved to be challenging across all models, with Spearman's rank correlation coefficients not exceeding 0.4, highlighting an area ripe for future exploration. Furthermore, except for BNN, all models showed a tendency towards overconfidence in observed confidence during testing. This overconfidence was also prevalent in 𝑇 ! uncertainty estimation, with most models presenting smaller uncertainties. Significantly, GPR and BNN predicted larger uncertainties, somewhat mitigating this overconfidence issue. Excluding data points with high uncertainty predictions from the analysis led to reduced errors, suggesting a potential avenue for enhancing model accuracy. Nonetheless, the prediction of high-𝑇 ! polymers remained low accuracy, primarily due to the insufficient presence of high-𝑇 ! data in the training sets, posing a significant challenge for data-driven high-𝑇 ! polymer design.