Simultaneous multimodal and multitask strategies for diverse biodegradable polymers powered by NMR data science

Abstract

As the world shifts toward a sustainable and circular economy, the demand is growing for materials that are both high-performing and environmentally responsible. Biodegradable polymers present a fundamental design challenge, as increased mechanical strength can impede breakdown, creating a trade-off that conventional strategies struggle to reconcile. Here we present a multimodal, multitask deep-learning framework that models mechanical performance and mass-loss behavior from molecular descriptors, thermal properties, and Nuclear Magnetic Resonance (NMR) signals. Within a panel of seven representative biodegradable polyesters relevant to marine/estuarine setting as 0.2-mm films and evaluated in vacuum-filtered estuary water, the framework jointly addresses multiple targets—strain at break, maximum stress, Young's Modulus, and 30-day endpoint mass loss—and highlights hierarchical molecular and dynamic features underlying the toughness–degradability balance. SHAP analysis revealed that features governing segmental rigidity and thermal properties jointly drive the trade-off between mass loss and mechanical performance. In particular, descriptors from Time Domain Nuclear Magnetic Resonance (TD-NMR) relaxometry and ¹³C/¹H NMR principal components consistently emerged as top predictors linking degradability with strain at break, maximum stress, and Young's Modulus. This framework establishes a generalizable route to integrate multimodal NMR-derived dynamics with machine learning for rational design of biodegradable polymers.