In this study, we employ three distinct datasets to evaluate UQ capabilities of ML models, focusing on the prediction of 𝑇 ! of homopolymers. Figure (b) presents the distribution of these datasets in the chemical space through a 2-D t-SNE plot. It is important to note that, although datasets 2 and 3 fall within the chemical space covered by dataset 1, this does not necessarily imply that they are entirely representative of or within the distribution of dataset 1. Here, we employ Tanimoto similarity analysis to compare the input vector (MFF) and confirm that datasets 2 and 3 indeed represent out-of-distribution data. This consideration is critical for assessing the generalizability and robustness of the ML models in handling potential out-of-distribution data.