ArticleChemical science2022
A graph representation of molecular ensembles for polymer property prediction.
Article in Chemical science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
33 citing papers in PubMed.
- Interpretable Machine Learning Prediction of the Dielectric Constant and Bandgap of Polymers for Flexible Electronics.Polymers · 2026Article
- Trust Beyond Accuracy: Conformal Uncertainty Quantification Reveals the Generalization Gap in Polymer Glass Transition Temperature Prediction.ACS omega · 2026Article
- Learning ordinality-aware multimodal representations for composite materials design.Nature communications · 2026Article
- PolyT-GNN: A Graph Neural Network Framework for Data-Driven Discovery of High-Temperature Two-Way Shape Memory Polymers.ACS applied materials & interfaces · 2026Article
- 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
- Graph-Based Machine Learning Identifies Oxygenated Block Polymer Replacements for Conventional Plastics and Elastics.Journal of the American Chemical Society · 2026Article
- Graph Neural Networks for Polymer Characterization and Property Prediction: Opportunities and Challenges.Journal of chemical information and modeling · 2026Review
- Review
- Review
- Graph Representations of Nanostructured Hedgehog Particles with Variable Complexity.Chemistry of materials : a publication of the American Chemical Society · 2025Article
- Deep learning for property prediction of natural fiber polymer composites.Scientific reports · 2025Article
- Hierarchical Sensing Framework for Polymer Degradation Monitoring: A Physics-Constrained Reinforcement Learning Framework for Programmable Material Discovery.Sensors (Basel, Switzerland) · 2025Article
- Molecular property prediction in the ultra-low data regime.Communications chemistry · 2025Article
- Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials.Nature communications · 2025Article
- A new strategy for Cas protein recognition based on graph neural networks and SMILES encoding.Scientific reports · 2025Article
- PolyCL: contrastive learning for polymer representation learningDigital discovery · 2025Article
- Automated BigSMILES conversion workflow and dataset for homopolymeric macromolecules.Scientific data · 2024Article
- Calculating Pairwise Similarity of Polymer Ensembles via Earth Mover's Distance.ACS polymers Au · 2024Article
- Large-Scale Glass-Transition Temperature Prediction with an Equivariant Neural Network for Screening Polymers.ACS omega · 2024Article
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
Abstract
Synthetic polymers are versatile and widely used materials. Similar to small organic molecules, a large chemical space of such materials is hypothetically accessible. Computational property prediction and virtual screening can accelerate polymer design by prioritizing candidates expected to have favorable properties. However, in contrast to organic molecules, polymers are often not well-defined single structures but an ensemble of similar molecules, which poses unique challenges to traditional chemical representations and machine learning approaches. Here, we introduce a graph representation of molecular ensembles and an associated graph neural network architecture that is tailored to polymer property prediction. We demonstrate that this approach captures critical features of polymeric materials, like chain architecture, monomer stoichiometry, and degree of polymerization, and achieves superior accuracy to off-the-shelf cheminformatics methodologies. While doing so, we built a dataset of simulated electron affinity and ionization potential values for >40k polymers with varying monomer composition, stoichiometry, and chain architecture, which may be used in the development of other tailored machine learning approaches. The dataset and machine learning models presented in this work pave the path toward new classes of algorithms for polymer informatics and, more broadly, introduce a framework for the modeling of molecular ensembles.
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.