ArticleACS omega2022
Machine Learning with Enormous "Synthetic" Data Sets: Predicting Glass Transition Temperature of Polyimides Using Graph Convolutional Neural Networks.
Article in ACS omega, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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Who cites it
12 citing papers in PubMed.
- Boosting the Prediction Accuracy of Glass Transition Temperature in Polyimides: A Hybrid Machine Learning Approach Integrating Morgan Fingerprints and Molecular Descriptors.Molecular informatics · 2026Article
- Graph Neural Networks for Polymer Characterization and Property Prediction: Opportunities and Challenges.Journal of chemical information and modeling · 2026Review
- Interpreting Molecular Descriptors for Glass Transition Temperature Prediction and Design of Polyimides.Materials (Basel, Switzerland) · 2025Article
- Machine Learning-Enabled Parallel Prediction and Co-Design of Polyimides for Tailored Glass Transition, Dielectric, and Bandgap Properties.ACS omega · 2025Article
- Deep learning for property prediction of natural fiber polymer composites.Scientific reports · 2025Article
- Article
- Polymer Informatics Method for Fast and Accurate Prediction of the Glass Transition Temperature from Chemical Structure.Macromolecules · 2025Article
- Machine Learning in Polymer Research.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Data-Driven Design of Novel Polymer Excipients for Pharmaceutical Amorphous Solid Dispersions.Bioconjugate chemistry · 2024Article
- Large-Scale Glass-Transition Temperature Prediction with an Equivariant Neural Network for Screening Polymers.ACS omega · 2024Article
- Gibbs-Helmholtz Graph Neural Network for the Prediction of Activity Coefficients of Polymer Solutions at Infinite Dilution.The journal of physical chemistry. A · 2023Article
- Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the present work, we address the problem of utilizing machine learning (ML) methods to predict the thermal properties of polymers by establishing "structure-property" relationships. Having focused on a particular class of heterocyclic polymers, namely polyimides (PIs), we developed a graph convolutional neural network (GCNN), being one of the most promising tools for working with big data, to predict the PI glass transition temperature
Identifiers
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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.