indispensable tools in predicting polymer properties and enabling the inverse design of polymers with target properties. By capturing complex relationships between polymer structure and properties, ML models accelerate the discovery and optimization of materials for specific applications. However, the reliability of ML predictions is as important as their accuracy, particularly for high-stakes applications. UQ provides essential insights into the confidence of ML models, ensuring that predictions are trustworthy and actionable. These datasets were divided into training (80%), validation (10%), and testing (10%) subsets.