ReviewAdvanced materials (Deerfield Beach, Fla.)2025
Machine Learning in Polymer Research.
Review in Advanced materials (Deerfield Beach, Fla.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 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
44 citing papers in PubMed.
- Review
- Data-Driven Development of Biomedical Hydrogels for Controlled Drug Delivery: Clinical Applications and Emerging Machine-Learning Approaches.Journal of functional biomaterials · 2026Review
- Machine Learning-Driven Nanoscale Synthesis for Electrocatalytic Performance: From Data-Driven Methodologies to Closed-Loop Optimization.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Machine Learning Prediction of Solvent-Assisted Depolymerization in Epoxy Covalent Adaptable Networks.ACS omega · 2026Article
- Spatiotemporal immunomodulation with programmable biomaterials to promote musculoskeletal tissue regeneration.Bioactive materials · 2026Review
- Natural polysaccharides targeting mitochondrial function for colorectal cancer prevention and treatment: mechanisms and nano-delivery strategies.Chinese medicine · 2026Review
- Glass Transition Prediction of Binary Copolymers Across Large Chemical Spaces Using Machine Learning and Physics-Based Modeling.Polymers · 2026Article
- Challenges and Vision for Standardization of Biopolymer Data Sets for Machine Learning.Biomacromolecules · 2026Review
- Potentials of Machine Learning in Predicting Key Features of Synthetic Antimicrobial Polymers.ACS polymers Au · 2026Article
- Predictive Modelling of Solvent Effects on Drug Incorporation into Polymeric Nanocarriers: A Machine Learning Approach.Macromolecular rapid communications · 2026Article
- Body-responsive shape-memory polymers for biomedical applications.Bioactive materials · 2026Review
- Artificial intelligence-assisted design and optimization of stimuli-responsive nanocarriers for smart drug delivery.Materials today. Bio · 2026Review
- Structure-Aware Machine Learning for Polymers: A Hierarchical Graph Network for Predicting Properties From Statistical Ensembles.Macromolecular rapid communications · 2026Article
- Challenges and Opportunities in Machine Learning for Light-Emitting Polymers.Macromolecular rapid communications · 2026Review
- Uncovering Key Characteristics of Antibacterial Peptides through Machine Learning.Macromolecular rapid communications · 2026Article
- BB-EIT: A Generalized Prediction Model for Protein Adsorption on Polymer Brushes Using Augmented Chemical Embeddings.ACS applied materials & interfaces · 2026Article
- Thermo-Sensitive Polymeric Networks for Next-Generation Wound Management: A Review.AAPS PharmSciTech · 2026Review
- Identifying Polymers that Bind or Reject Proteins with Machine Learning: Handling Categorical Features within a GPR Model.ACS polymers Au · 2026Article
- Graph-Based Machine Learning Identifies Oxygenated Block Polymer Replacements for Conventional Plastics and Elastics.Journal of the American Chemical Society · 2026Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Machine learning is increasingly being applied in polymer chemistry to link chemical structures to macroscopic properties of polymers and to identify chemical patterns in the polymer structures that help improve specific properties. To facilitate this, a chemical dataset needs to be translated into machine readable descriptors. However, limited and inadequately curated datasets, broad molecular weight distributions, and irregular polymer configurations pose significant challenges. Most off the shelf mathematical models often need refinement for specific applications. Addressing these challenges demand a close collaboration between chemists and mathematicians as chemists must formulate research questions in mathematical terms while mathematicians are required to refine models for specific applications. This review unites both disciplines to address dataset curation hurdles and highlight advances in polymer synthesis and modeling that enhance data availability. It then surveys ML approaches used to predict solid-state properties, solution behavior, composite performance, and emerging applications such as drug delivery and the polymer-biology interface. A perspective of the field is concluded and the importance of FAIR (findability, accessibility, interoperability, and reusability) data and the integration of polymer theory and data are discussed, and the thoughts on the machine-human interface are shared.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.