Polymer Informatics in the Era of Foundation Models: From Representation to Autonomous Design
Year: 2026
Type: preprint
Abstract: Foundation models, pretrained on broad datasets and adapted to multiple tasks, offer a way to reuse chemical knowledge across polymer property prediction and inverse design. Realizing this opportunity requires connecting learned representations to materials whose properties also depend on molecular populations, formulation, processing, and measurement conditions. Here, we review advances in polymer foundation models and the data, representations, and experimental workflows that support their use in materials design. We organize these developments around the polymer material state, a description linking chemical identity to molecular distributions, architecture, morphology, and preparation history relevant to a target property. This organization connects data resources and pretraining strategies to questions about what models learn, how that knowledge transfers, and which experimental choices it can support. We compare learning objectives and adaptation methods, examine representations of polymer chemistry and state, and follow inverse design from candidate generation through synthesis and processing to validation. Pretrained models now support prediction across multiple properties and generation for specified targets, with selected polymer candidates reaching synthesis and thermal characterization. Broader polymer design campaigns show how precursor accessibility, polymerization outcomes, and specimen preparation influence whether a predicted advantage becomes a measured material response. Building on this evidence, we discuss opportunities to combine chemical pretraining with richer sample information and to assess transfer across polymer families, preparation conditions, and experimental campaigns. The resulting synthesis connects model development to a practical objective for polymer informatics: reducing the new experimentation needed to obtain materials with reproducible, targeted performance.
Source: ChemRxiv
Authors: Jianxin He, Ying Li
Institution: University of Wisconsin–Madison
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: green
Awards: CLIMA/Collaborative Research: Discovery of Covalent Adaptable Networks for Sustainable Manufacturing and Recycling of Wind Turbine Blades, G3387330441, G4754425734, G5548695524, CAREER: Machine Learned Coarse-grained Modeling for Mechanics of Thermoplastic Elastomers, G6671297155, G6704618686, Unraveling Mechanics of High Strength and Low Stiffness in Polymer Nanocomposites through Integrated Molecular Modeling and Nanomechanical Experiments, G8517073479, G8559326691
OpenAlex ID: W7214107647