When ML models trained with experimental data are applied to dataset 3, a notable decrease in prediction accuracy is observed, as illustrated in Figure . GPR stands out as the only model achieving an 𝑅² above 0. We employed six ML models to predict the mean and standard deviation of 𝑇 ! values for these polymers, with the outcomes detailed in Table . The parity plots for high-𝑇 ! polymer prediction is shown in Supporting Information, S5. It was observed that the means predicted by all models were consistently lower than the experimental values. GPR and BNN demonstrated the capability to predict larger standard deviations, reflecting greater uncertainty, consistent with the observations from datasets 1, 2, and 3. The remaining four methods displayed overconfidence, evidenced by smaller standard deviations. This overconfidence could stem from the models' inability to adequately capture the complex behavior of high-𝑇 ! polymers or from a lack of diversity in the training data that leads to an underestimation of the predictive uncertainty.