ArticleACS applied materials & interfaces2026
PolyT-GNN: A Graph Neural Network Framework for Data-Driven Discovery of High-Temperature Two-Way Shape Memory Polymers.
Article in ACS applied materials & interfaces, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Two-way shape memory polymers (2W-SMPs) represent a promising class of smart materials capable of reversible actuation under cyclic thermal stimuli. Despite their potential for high-performance applications in aerospace, geothermal, self-healing, biomedical, and soft robotics, the discovery of new 2W-SMPs remains limited by the lack of large, systematic data sets and predictive design strategies, especially for high-temperature systems. Here, PolyT-GNN is introduced as a graph neural network framework for data-driven discovery of high-temperature 2W-SMPs. A curated data set of 170 experimentally validated 2W-SMPs is compiled from the literature. PolyT-GNN integrates atomic, bond, and molecular descriptors with explicit monomer weight ratios to accurately predict polymer transition temperatures, achieving a test
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