AI-Driven prediction of polymer properties: Biodegradability, strength, and diffusion

The computational prediction of polymer properties represents a transformative frontier in materials science, leveraging machine learning to accelerate the discovery and optimization of advanced polymeric materials. This comprehensive review examines state-of-the-art artificial intelligence methodologies for predicting three critical polymer properties: biodegradability, mechanical strength, and gas diffusion characteristics. Recent advances in graph neural networks, physics-informed neural networks, and multi-task learning frameworks have achieved unprecedented prediction accuracies (R² > 0.96) while addressing fundamental challenges in data scarcity, chemical space extrapolation, and interpretability [1-3]. This paper synthesizes current knowledge, presents quantitative performance metrics, and discusses future research directions in AI-driven polymer informatics.