Prediction and Interpretation of Polymer Properties Using the Graph Convolutional Network.

ABSTRACT: We present machine learning models for the prediction of thermal and mechanical properties of polymers based on the graph convolutional network (GCN). GCN-based models provide reliable prediction performances for the glass transition temperature (T _g), melting temperature (T _m), density (ρ), and elastic modulus (E) with substantial dependence on the dataset, which is the best for T _g (R ² ∼ 0.9) and worst for E (R ² ∼ 0.5). It is found that the GCN representations for polymers provide prediction performances of their properties comparable to the popular extended-connectivity circular fingerprint (ECFP) representation. Notably, the GCN combined with the neural network regression (GCN-NN)

SUBMITTER: Park J 

PROVIDER: S-EPMC9954297 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature