Experimentally measuring material's properties is resource-intensive, whereas ML offers an efficient alternative for predicting these properties using existing data through material informatics 66 . However, the reliability of these predictions, particularly for new polymer candidates, hinges on the confidence levels provided by ML models. This confidence is crucial in determining whether these candidates should proceed to experimental validation for mechanical properties and material stability. Often, these new materials represent out-of-distribution data, diverging from the chemical space covered by the training dataset. Consequently, the generalization capability of ML models becomes a critical aspect of the UQ for 𝑇 ! predictions, ensuring the applicability and reliability of the ML models in identifying viable new materials. To evaluate the generalization capability of the six ML models trained on experimental data from PoLyInfo, we utilized an additional 240 experimental data points (dataset 2).