The ideal model should not only exhibit high accuracy, as measured by traditional metrics (MAE, RMSE, and 𝑅²), but also demonstrate a positive correlation between predicted uncertainties and actual errors. This balance ensures that high-confidence predictions correspond to lower errors, aiding in the assessment of model confidence and reliability. Before assessing the UQ models, it is essential first to evaluate the accuracy of these ML models for predicting the polymer's 𝑇 ! . A detailed benchmark for 𝑇 ! prediction in polymers is available for reference. For polymer 𝑇 ! prediction of testing set, ensemble and BNN models exhibit superior performance, with the lowest errors in terms of MAE and RMSE. The 𝑅² scores for both models exceed 0.91, indicating a high level of accuracy, as shown in Figure . The other four methods also maintain high accuracy, with 𝑅² values above 0.90, affirming their suitability for predicting 𝑇 ! uncertainty.