Data‐augmented machine learning for inverse design of homopolymers with targeted glass transition temperature

Abstract Establishing a reliable workflow of inverse design by data‐driven machine learning (ML) models offers significant potential to accelerate molecular design of polymeric materials. Nevertheless, there exist scarcity issues of training datasets in current data‐driven models for polymers. In this contribution, we integrate the ML method with a data augmentation strategy to build upon a workflow of inverse design of polymeric materials with targeted glass transition temperature T g . Results show that the data‐augmented ML model significantly enhances the prediction accuracy of T g in spite of a small training dataset. Furthermore, the data augmentation strategy has the capability of generating the monomers of homopolymers with higher novelty and uniqueness, whose T g values are validated by the simulations of all‐atomic molecular dynamics. The ML‐assisted inverse design workflow offers significant advantages in establishing structure–property relationships and also provides an accelerated pathway for the targeted design of polymer systems. © 2025 Society of Chemical Industry.