Utilizing three datasets and 19 high-𝑇 ! polymers sourced from PoLyInfo 40 and journal articles, allows us to conduct a comprehensive assessment of different UQ methods for polymer's 𝑇 ! prediction. This evaluation is important for effectively addressing out-of-distribution detection, a paramount challenge in ensuring the network's decisions remain reliable and predictable. To enrich the molecular information, simplified molecular input line entry system (SMILES) notations of the repeat units are employed, offering insights into the interconnection points between monomers of homopolymers. The Morgan fingerprint with frequency (MFF) serves as the representation format, forming the input for ML models, to consider the number of occurrences for certain substructures. Three metrics are selected to assess the performance of 𝑇 ! predictions considering the epistemic uncertainties, providing a comprehensive benchmark for UQ on polymer's 𝑇 ! prediction. The workflow of this study is illustrated in Figure .