Polymer graph neural networks for multitask property learning

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.