In contrast, 𝑇 𝑔 𝑀𝐷 , derived from MD simulations, introduces biases due to approximations in simulation protocols, such as limitations in force field accuracy and assumptions about polymer behavior. These biases reduce prediction accuracy and challenge the ability of UQ methods to correlate predicted uncertainties with actual errors. Despite these challenges, MD-derived data offer valuable insights when integrated with high-fidelity experimental data for the prediction in polymer discovery. By addressing biases in MD simulations, such as improving force field accuracy or employing ML-based corrections to align simulation outputs with experimental trends, the utility of MD data can be enhanced. Incorporating MD data into hybrid workflows with experimental data provides a complementary approach, leveraging the scalability of lowfidelity MD data and the reliability of high-fidelity experimental measurements. This integration enables ML models to generalize better across a broader chemical space, facilitating the discovery of novel polymers. Accurate predictions for these materials enable the identification of candidates suitable for extreme operating conditions. Similarly, special polymer types, including polyacrylics, polyamides/thioamides, polyesters/thiolesters, and polyvinyls, represent distinct chemical classes with specific functionalities tailored for applications in textiles, biomedical devices, and specialty coatings. For both cases, UQ is indispensable to ensure reliability in predictions, particularly when exploring new design spaces for functional polymers. The percentages indicate the proportion of each polymer type within the 𝑇 𝑔 dataset.