The 𝑇 𝑔 𝑀𝐷 dataset, derived from MD simulations, introduces additional complexity due to the inherent approximations in computational protocols. These simulations rely on PCFF force fields and assumptions about polymer behavior, leading to discrepancies between simulated and experimental results. However, as with 𝑇 𝑔 𝐸𝑋𝑃 , GPR exhibited weak UQ performance, with near-zero Spearman's coefficients, indicating limited correlation between uncertainties and errors. BNN and EDL achieved R 2 values between 0.55 and 0.60, while Ensemble, MVE and MCD had R 2 below 0.55, reflecting lower predictive accuracy. In terms of UQ metrics, BNN provided the highest Spearman's coefficients (ρ=0.165), suggesting a modest relationship between predicted uncertainties and actual errors. However, the overall lower Spearman's coefficient values across all models highlight the challenges of capturing uncertainty for MD-derived datasets. Calibration analysis revealed overconfidence for all models, though GPR maintained relatively better calibration due to its ability to scale uncertainties effectively.