Property Prediction of Bio-Derived Block Copolymer Thermoplastic Elastomers Using Graph Kernel Methods

Increasing the diversity of bio-based polymers is needed to address the combined problems of plastic pollution and greenhouse gas emissions. The magnitude of the problems necessitates rapid discovery of new materials; however, identification of appropriate chemistries maybe slow using current iterative methods. Machine learning (ML) methods could significantly expedite new material discovery and property identification. Here, PolyAGM, a ML algorithm using graph kernel methods, is introduced and used to predict the properties of block copolymers and identify the responsible structural 'motifs'. It applies a "fingerprinting" method to convert Graph representations of polymers into numerical vectors. The Graphs explicitly encode the entire copolymer of atoms and bonds such that the sequencing of chemical features and polymer chain length are included, alongside relevant stereochemical information. PolyAGM gives predictions for both thermal and mechanical properties that are in good agreement with experimental measurements. This work focuses on predicting the properties of bio-derived ABA-block polymer thermoplastic elastomers, but the general fingerprinting technique of PolyAGM should be relevant to other application fields.