Screening Diels–Alder reaction space to identify candidate reactions for self-healing polymer applications

Abstract

Plastics are essential in modern society, but their susceptibility to damage limits their lifespan and performance, and results in unsustainable waste production. Self-healing polymers based on thermally reversible Diels–Alder (DA) reactions offer a potential solution by enabling heating controlled repair through bond-breaking and reformation. However, discovering new suitable DA monomer combinations has largely relied on intuition and trial-and-error so far. Here, we present a hierarchical workflow that integrates machine learning (ML) with automated reaction profile calculations to efficiently screen DA reactions for self-healing polymer applications. Using our in-house TS-tools software, we generate high-throughput profiles at the semi-empirical xTB level. Refining only a small fraction with DFT, we are able to train a robust ML model that predicts reaction characteristics with excellent accuracy. Adding a graph-based ML model to the workflow for pre-screening enables expansion to reaction spaces of hundreds of thousands of reactions, at a marginal cost. We first leverage our models to screen a comprehensive reaction space of synthetic diene–dienophile pairs, and subsequently use them to mine a database of commercially available natural products. Overall, this hybrid ML-computational chemistry approach enables data-efficient discovery of thermally responsive DA reactions, advancing the rational design of self-healing polymers with tunable properties.