Rational Design of Sustainable Bio-based Thermosets: Integrating Experiments, Simulations, and Machine Learning for Enhanced Performance

• Applicant: Nataliya Kiriy, Ph.D.
• Subject Area: Polymer Materials
• Term: since 2026
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 573087814

This project aims to bridge critical experimental and computational gaps in the development of sustainable bio-based thermosets by integrating monomer synthesis, network design with recyclability, molecular simulations, and machine learning (ML)-guided materials design. The approach combines the synthesis of two established petroleum-based thermoset reference systems with the replacement of petrochemical components by renewable building blocks to create a diverse library of functional bio-based resins. These resins will be cured into thermoset networks, whose mechanical properties and recyclability—enhanced by dynamic covalent bonds—will be systematically evaluated. To deepen understanding of the relationships between network structure, properties, and recyclability, we will employ a molecular modeling framework that couples kinetic Monte Carlo simulations for curing kinetics with molecular dynamics to predict thermomechanical behavior. Experimental data will feed a digital database used to train ML models that predict material performance across unexplored formulation spaces. Crucially, an iterative closed-loop workflow will link ML predictions with experimental validation, continuously refining model accuracy and accelerating the rational design of high-performance, recyclable bio-based thermosets. This interdisciplinary approach lays the groundwork for next-generation sustainable thermoset materials that meet industrial requirements while advancing circular material strategies.

• DFG Programme: Research Grants
• International Connection: Singapore
• Partner Organisation: Agency for Science, Technology and Research (A*STAR)
• Cooperation Partner: Nannan Li, Ph.D.