Gibbs-Helmholtz Graph Neural Network for the Prediction of Activity Coefficients of Polymer Solutions at Infinite Dilution.

ABSTRACT: Machine learning models have gained prominence for predicting pure-component properties, yet their application to mixture property prediction remains relatively limited. However, the significance of mixtures in our daily lives is undeniable, particularly in industries such as polymer processing. This study presents a modification of the Gibbs-Helmholtz graph neural network (GH-GNN) model for predicting weight-based activity coefficients at infinite dilution (Ω_ij^∞) in polymer solutions. We evaluate various polymer representations ranging from monomer, repeating unit, periodic unit, and oligomer and observe that, in data-scarce scenarios of polymer-solvent mixtures, polymer representation specifics have a reduced impact compared to data-rich environments. Leverag

SUBMITTER: Sanchez Medina EI 

PROVIDER: S-EPMC10683018 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature