ReviewPolymers2025
Boosting-Based Machine Learning Applications in Polymer Science: A Review.
Review in Polymers, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
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Who cites it
13 citing papers in PubMed.
- Leveraging Machine Learning for Accelerated Electrode-Electrolyte Interface Design in Rechargeable Li-Based Batteries.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Food Production Index Forecasting for Sustainable Food Systems in Türkiye: A Machine Learning-Based Approach.Foods (Basel, Switzerland) · 2026Article
- Article
- Prediction of Bond Strength in Corroded Reinforced Concrete Using SVM and XGB Methods.Materials (Basel, Switzerland) · 2026Article
- Leveraging molecular descriptors and explainable machine learning for monomer conversion prediction in photoinduced electron transfer-reversible addition-fragmentation chain transfer polymerization.Scientific reports · 2026Article
- Development and external validation of machine-learning models for diagnosing malignant pleural effusion.Frontiers in oncology · 2026Article
- Sustainable design of jewelry materials through green polymer and low impact processing strategies.Frontiers in chemistry · 2026Article
- Hybrid experimental and machine learning approach for optimizing abrasive wear of microcrystalline cellulose modified hemp/bamboo fiber composites.Scientific reports · 2025Article
- Article
- Machine learning-based prognosis prediction for serous ovarian cancer using the SEER database and data from a single center in China.Translational cancer research · 2025Article
- Advanced machine learning models for the prediction of ceramic tiles' properties during the firing stage.Scientific reports · 2025Article
- Development of an AI-Empowered Novel Digital Monitoring System for Inhalation Flow Profiles.Sensors (Basel, Switzerland) · 2025Article
- Optimization of Hierarchical Groove-Perforation Structures in PET Foam Cores for Wind Turbine Blade Applications.Materials (Basel, Switzerland) · 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The increasing complexity of polymer systems in both experimental and computational studies has led to an expanding interest in machine learning (ML) methods to aid in data analysis, material design, and predictive modeling. Among the various ML approaches, boosting methods, including AdaBoost, Gradient Boosting, XGBoost, CatBoost and LightGBM, have emerged as powerful tools for tackling high-dimensional and complex problems in polymer science. This paper provides an overview of the applications of boosting methods in polymer science, highlighting their contributions to areas such as structure-property relationships, polymer synthesis, performance prediction, and material characterization. By examining recent case studies on the applications of boosting techniques in polymer science, this review aims to highlight their potential for advancing the design, characterization, and optimization of polymer materials.
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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.