Fig. 1: AI-assisted design of polymers for energy storage.

a Our four-step design approach. First, generate a pool of chemical structures. Then, predict the properties of each. Next, use the predicted properties to screen for the best candidates. Finally, synthesize and characterize the selected candidates. b Chemical structures are generated in three steps. First, curate a database of available monomers. Then, choose a reaction. Use that reaction to select/reject each monomer. Finally, the selected monomers are chemically-transformed from a monomer into a polymer repeat unit. c Structure-property models are trained using multitask graph neural networks³³. The starting point is an example dataset containing labeled pairs of the form [Structure, Properties]. Then, each chemical structure is converted to a machine-readable graph, with heavy atoms as nodes and covalent bonds as edges. A model is trained on the data to establish a mapping between a structure’s graph and each property. An intermediate output, also learned during training, is the fingerprint. Properties are predicted for each chemical structure in the polyVERSE database. d Screening is performed using a sequence of carefully chosen, application-specific, filters: high glass-transition temperature, band gap and dielectric constant. In the figure, GNN stands for graph neural network and MLP stands for multi-layer perceptron.