摘要：SummaryEstablishing the structure-property relationship is extremely valuable for the molecular design of copolymers. However, machine learning (ML) models can incorporate both chemical composition and sequence distribution of monomers, and have the generalization ability to process various copolymer types (e.g., alternating, random, block, and gradient copolymers) with a unified approach are missing. To address this challenge, we formulate four different ML models for investigation, including a feedforward neural network (FFNN) model, a convolutional neural network (CNN) model, a recurrent neural network (RNN) model, and a combined FFNN/RNN (Fusion) model. We use various copolymer types to systematically validate the performance and generalizability of different models. We find that the RNN architecture that processes the monomer sequence information both forward and backward is a more suitable ML model for copolymers with better generalizability. As a supplement to polymer informatics, our proposed approach provides an efficient way for the evaluation of copolymers.Graphical abstractDisplay OmittedHighlights•Establish structure-property relationships of copolymer with machine learning (ML)•Incorporate both chemical composition and sequential distribution of copolymers•Analyze various copolymer types with different models in a unified approach•Differentiate the effects of random, block, and gradient patterns of copolymersArtificial intelligence; Materials science; Polymers
