Cross-Scale Machine Learning for Polymer Materials: Linking Molecular Structure, Mesoscale Organization, Processing History, and Macroscopic Properties

Abstract

Machine learning (ML) is increasingly used to predict polymer properties, screen candidates, and monitor manufacturing. Yet many studies remain confined to direct repeat-unit-to-property or process-to-quality correlations, while explicit state variables are often predicted only as terminal targets, treated as parallel objectives, or invoked after prediction. Here, mediator-resolved cross-scale polymer machine learning is reserved for workflows in which a condensed-state or process-state variable is measured, predicted, or physically constrained and is used to connect upstream material identity or processing history to a downstream macroscopic response or decision. We organize the literature around four links: polymer identity and statistical chain structure; condensed-state and mesoscale organization; processing history; and macroscopic response. Rather than ranking methods by headline accuracy, we compare representation fidelity, data provenance, splitting strategy, uncertainty treatment, external or experimental validation, and reproducibility. The evidence indicates that repeat-unit representations remain useful for chemical-space screening but are insufficient for sample-specific predictions unless task-relevant information on molar-mass distribution, sequence, topology, preparation, and test conditions is included. Random splits often overstate generalization, and feature-attribution methods identify model correlations rather than mechanisms. Injection molding and extrusion are examined as detailed processing cases, with other routes considered to delineate common process–structure–property requirements. We conclude with priorities for metadata standards, multimodal benchmarks, uncertainty-aware physics-integrated models, and closed-loop validation. The resulting framework distinguishes mature interpolation tasks from genuinely transferable cross-scale design workflows.