2026, Vol. 7, Issue 1, Part A
AI-Driven prediction of polymer properties: Biodegradability, strength, and diffusion
Author(s): Tamanna Begam
Abstract: 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.
DOI: 10.22271/reschem.2026.v7.i1a.243
Pages: 08-15 | Views: 77 | Downloads: 39
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How to cite this article:
Tamanna Begam. AI-Driven prediction of polymer properties: Biodegradability, strength, and diffusion. J Res Chem 2026;7(1):08-15. DOI: 10.22271/reschem.2026.v7.i1a.243
