Polymer graph neural networks for multitask property learning

• Published in: npj computational materials 2023-05, Vol.9 (1), p.90-10
• Main Authors: Queen, Owen, McCarver, Gavin A., Thatigotla, Saitheeraj, Abolins, Brendan P., Brown, Cameron L., Maroulas, Vasileios, Vogiatzis, Konstantinos D.
• Format: Article
• Language: English
• Subjects: 639/301/923/1028; 639/638/455/958; 639/638/563; Acids; Characterization and Evaluation of Materials; Chemistry and Materials Science; Composition; Computational Intelligence; Copolymers; Datasets; Graph neural networks; Informatics; Machine learning; Materials Science; Mathematical and Computational Engineering; Mathematical and Computational Physics; Mathematical Modeling and Industrial Mathematics; Molecular weight; Monomers; Neural networks; Polyester resins; Polyesters; Polyethylene terephthalate; Polymers; Resins; Theoretical
• ISSN: 2057-3960
• DOI: 10.1038/s41524-023-01034-3

The prediction of a variety of polymer properties from their monomer composition has been a challenge for material informatics, and their development can lead to a more effective exploration of the material space. In this work, PolymerGNN , a multitask machine learning architecture that relies on polymeric features and graph neural networks has been developed towards this goal. PolymerGNN provides accurate estimates for polymer properties based on a database of complex and heterogeneous polyesters (linear/branched, homopolymers/copolymers) with experimentally refined properties. In PolymerGNN , each polyester is represented as a set of monomer units, which are introduced as molecular graphs. A virtual screening of a large, computationally generated database with materials of variable composition was performed, a task that demonstrates the applicability of the PolymerGNN on future studies that target the exploration of the polymer space. Finally, a discussion on the explainability of the models is provided.