[2603.02810] ChemFlow:A Hierarchical Neural Network for Multiscale Representation Learning in Chemical Mixtures

Physics > Chemical Physics arXiv:2603.02810 (physics) [Submitted on 3 Mar 2026] Title:ChemFlow:A Hierarchical Neural Network for Multiscale Representation Learning in Chemical Mixtures Authors:Jinming Fan, Chao Qian, Wilhelm T. S. Huck, William E. Robinson, Shaodong Zhou View a PDF of the paper titled ChemFlow:A Hierarchical Neural Network for Multiscale Representation Learning in Chemical Mixtures, by Jinming Fan and 4 other authors View PDF Abstract:Accurate prediction of the physicochemical properties of molecular mixtures using graph neural networks remains a significant challenge, as it requires simultaneous embedding of intramolecular interactions while accounting for mixture composition (i.e., concentrations and ratios). Existing approaches are ill-equipped to emulate realistic mixture environments, where densely coupled interactions propagate across hierarchical levels - from atoms and functional groups to entire molecules - and where cross-level information exchange is continuously modulated by composition. To bridge the gap between isolated molecules and realistic chemical environments, we present ChemFlow, a novel hierarchical framework that integrates atomic, functional group, and molecular-level features, facilitating information flow across these levels to predict the behavior of complex chemical mixtures. ChemFlow employs an atomic-level feature fusion module, Chem-embed, to generate context-aware atomic representations influenced by the mixture state and at...