Ensemble excels in accuracy for specialized high-𝑇 𝑔 applications, while BNN's robust UQ and adaptability make it the preferred model for broader use cases. These findings demonstrate the critical role of UQ in ML workflows, emphasizing its importance in guiding high-confidence predictions, reducing the risk and cost of experimental validation, and accelerating the design and discovery of advanced polymer materials.