ArticlePolymers2024
Interpretable Machine Learning Framework to Predict the Glass Transition Temperature of Polymers.
Article in Polymers, 2024. 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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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.
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Who cites it
12 citing papers in PubMed.
- Article
- Experimental Study and Numerical Modeling of Thermoviscoelastic Behavior of Antifriction Polymeric Materials.Polymers · 2026Article
- Thermo-Sensitive Polymeric Networks for Next-Generation Wound Management: A Review.AAPS PharmSciTech · 2026Review
- Leveraging molecular descriptors and explainable machine learning for monomer conversion prediction in photoinduced electron transfer-reversible addition-fragmentation chain transfer polymerization.Scientific reports · 2026Article
- The Alchemist, the Scientist, and the Robot: Exploring the Potential of Human-AI Symbiosis in Self-Driving Polymer Laboratories.Macromolecular rapid communications · 2025Review
- Machine learning and Mendelian randomization identify key lifestyle factors in coronary heart disease: A NHANES-Based study.International journal of cardiology. Cardiovascular risk and prevention · 2025Article
- The Polymer Physics and Theory Section.Polymers · 2025Article
- Article
- Machine Learning in Polymer Research.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Support Vector Machines in Polymer Science: A Review.Polymers · 2025Review
- Prediction Trough Concentrations of Valproic Acid Among Chinese Adult Patients with Epilepsy Using Machine Learning Techniques.Pharmaceutical research · 2025Article
- Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The glass transition temperature of polymers is a key parameter in meeting the application requirements for energy absorption. Previous studies have provided some data from slow, expensive trial-and-error procedures. By recognizing these data, machine learning algorithms are able to extract valuable knowledge and disclose essential insights. In this study, a dataset of 7174 samples was utilized. The polymers were numerically represented using two methods: Morgan fingerprint and molecular descriptor. During preprocessing, the dataset was scaled using a standard scaler technique. We removed the features with small variance from the dataset and used the Pearson correlation technique to exclude the features that were highly connected. Then, the most significant features were selected using the recursive feature elimination method. Nine machine learning techniques were employed to predict the glass transition temperature and tune their hyperparameters. The models were compared using the performance metrics of mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R
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Registered trials
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