ReviewACS polymers Au2023
Emerging Trends in Machine Learning: A Polymer Perspective.
Review in ACS polymers Au, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
What it found
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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.
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Who cites it
30 citing papers in PubMed.
- Multimodal Transformer for Sample-Aware Prediction of Metal-Organic Framework Properties.ACS central science · 2026Article
- Article
- Challenges and Vision for Standardization of Biopolymer Data Sets for Machine Learning.Biomacromolecules · 2026Review
- Self-driving lab for the data-driven design of single-chain polymer nanoparticles.Cell press blue · 2026Article
- Challenges and Opportunities in Machine Learning for Light-Emitting Polymers.Macromolecular rapid communications · 2026Review
- Role of High Fidelity Vs. Low Fidelity Experimental Data in Machine Learning Model Performance for Predicting Polymer Solubility.Macromolecular rapid communications · 2026Article
- Identifying Polymers that Bind or Reject Proteins with Machine Learning: Handling Categorical Features within a GPR Model.ACS polymers Au · 2026Article
- Graph Neural Networks for Polymer Characterization and Property Prediction: Opportunities and Challenges.Journal of chemical information and modeling · 2026Review
- Cracking the code of multi-layer films to promote circularity in single-use plastic packaging.Nature communications · 2026Review
- Artificial Intelligence Informed Hydrogel Biomaterials in Additive Manufacturing.Gels (Basel, Switzerland) · 2025Review
- The Alchemist, the Scientist, and the Robot: Exploring the Potential of Human-AI Symbiosis in Self-Driving Polymer Laboratories.Macromolecular rapid communications · 2025Review
- LiteBoost: a lightweight and explainable boosting model for predicting polymer density from SMILES data.Journal of computer-aided molecular design · 2025Article
- Atomistic Polymer Modeling: Recent Advances and Challenges in Building and Parametrization Workflows.Macromolecules · 2025Review
- PMSE Centennial: Celebration of Success and New Frontiers in Polymer Materials Science and Engineering.ACS macro letters · 2025Article
- Exploiting Machine Learning and Automated Synthesis in Continuous Flow for Process Optimization of the Organocatalyzed Ring-Opening Polymerization of l‑Lactide.ACS polymers Au · 2025Article
- Understanding Viscoelasticity of an Entangled Silicone Copolymer via Coarse-Grained Molecular Dynamics Simulations.Macromolecules · 2025Article
- Deep learning for property prediction of natural fiber polymer composites.Scientific reports · 2025Article
- Review
- Synchrotron Radiation X‑ray Scattering Approaching Real Industrial Processing of Polymer.Polymer science & technology (Washington, D.C.) · 2025Review
- Optimization of glutaraldehyde concentration in relation to swelling behavior of PVA-PEG-BTB film using hybrid genetic metaheuristic algorithm.Scientific reports · 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the last five years, there has been tremendous growth in machine learning and artificial intelligence as applied to polymer science. Here, we highlight the unique challenges presented by polymers and how the field is addressing them. We focus on emerging trends with an emphasis on topics that have received less attention in the review literature. Finally, we provide an outlook for the field, outline important growth areas in machine learning and artificial intelligence for polymer science and discuss important advances from the greater material science community.
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.