ReviewPolymers2025
Recent Progress of Artificial Intelligence Application in Polymer Materials.
Review in Polymers, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Article
- Review
- Boosting the Prediction Accuracy of Glass Transition Temperature in Polyimides: A Hybrid Machine Learning Approach Integrating Morgan Fingerprints and Molecular Descriptors.Molecular informatics · 2026Article
- Optimizing Process Parameters in Laser Transmission Welding Solid PC/Porous-PET Using Prediction Models: Experimental Validation and Morphology Analysis.Materials (Basel, Switzerland) · 2026Article
- Functional Materials for Additive Manufacturing: Materials Design, Processing, and Emerging Applications.Nanomaterials (Basel, Switzerland) · 2026Review
- Nano- and Micro-Polymer Fibers for Smart Actuation: Fabrication Methods and Applications-A Review.Gels (Basel, Switzerland) · 2026Review
- Decoding Structure-Property Relationships in Anion Exchange Membranes via a Chemically Informed Dual-Channel Graph Attention Network.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Physics-informed AI framework for multiscale design, discovery, and optimization of polymer nanocomposite microstructures using neural operators.Scientific reports · 2026Article
- Cyclodextrin-Silica Hybrid PEG Hydrogels: Mechanistic Coupling Between Stiffness, Relaxation, and Molecular Transport.Gels (Basel, Switzerland) · 2026Review
- AI in Membrane Design and Optimization for Hydrogen Fuel Cells.Membranes · 2026Review
- Nanotechnology-Driven Cancer Therapies for Precision Oncology: Advances and Clinical Outlook.International journal of nanomedicine · 2026Review
- AI-Driven Polymeric Coatings: Strategies for Material Selection and Performance Evaluation in Structural Applications.Polymers · 2025Review
- Review
- Scientific Machine Learning for Polymeric Materials.Polymers · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Artificial intelligence (AI) technology has made remarkable progress in polymer materials, which has changed polymer science significantly. However, this community still relies heavily on the traditional research paradigm instead of the data-driven paradigm. This review advocates for a fundamental paradigm shift in polymer research from traditional experience-driven methods to data-driven approaches enabled by AI. While AI has made transformative advances in polymer design, property prediction, and process optimization, the field remains anchored in conventional methodologies. AI's computational advantages against persistent barriers are also evaluated, such as data scarcity, inadequate material descriptors, and algorithmic complexity. Potential solutions, including collaborative data platforms, domain-adapted descriptor frameworks, and active learning strategies, are also discussed. Furthermore, we demonstrate how high-quality data and explainable AI methodologies overcome computational limitations while ensuring result credibility in other areas, which can benefit polymer research. Ultimately, this work provides a roadmap for accelerating the sustainable convergence of data-driven AI innovation with polymer science.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.