ReviewPolymers2024
Applications of Long Short-Term Memory (LSTM) Networks in Polymeric Sciences: A Review.
Review in Polymers, 2024. 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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Who cites it
13 citing papers in PubMed.
- Predictive Modeling of Mining Laboratory Effluent Contamination Using LSTM-Attention Networks: A Case Study from the Haut-Katanga Copperbelt.Environmental geochemistry and health · 2026Article
- Deep Learning-Based Prediction and Compensation of Performance Degradation in Flexible Sensors.Micromachines · 2026Article
- PrivEdge: a hybrid split-federated learning framework for real-time electricity theft detection on edge nodes.Scientific reports · 2026Article
- Advances in AI-based diagnosis of Alzheimer's disease using MRI: a comprehensive survey.Frontiers in medicine · 2026Review
- A deep learning approach to mild cognitive impairment detection from electroencephalogram signals.Dementia & neuropsychologia · 2026Article
- Hybrid fuzzy clustering and temporal deep learning framework for multi-parameter forecasting in industrial thermal processes.Frontiers in artificial intelligence · 2026Article
- Machine learning analysis based on deep learning for fatigue diagnostics in carbon fiber reinforced polymers.PloS one · 2026Article
- Fatigue Life Prediction of CFRP-FBG Sensor-Reinforced RC Beams Enabled by LSTM-Based Deep Learning.Polymers · 2025Article
- Article
- Experimental Evaluation and Machine Learning-Based Prediction of Laser Cutting Quality in FFF-Printed ABS Thermoplastics.Polymers · 2025Article
- Research on encrypted malicious traffic detection in power information interaction: application of the electricity multi-granularity flow representation learning approach.Scientific reports · 2025Article
- Application and comparison of ARIMA, LSTM, and ARIMA-LSTM models for predicting foodborne diseases in Liaoning Province.Frontiers in big data · 2025Article
- Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This review explores the application of Long Short-Term Memory (LSTM) networks, a specialized type of recurrent neural network (RNN), in the field of polymeric sciences. LSTM networks have shown notable effectiveness in modeling sequential data and predicting time-series outcomes, which are essential for understanding complex molecular structures and dynamic processes in polymers. This review delves into the use of LSTM models for predicting polymer properties, monitoring polymerization processes, and evaluating the degradation and mechanical performance of polymers. Additionally, it addresses the challenges related to data availability and interpretability. Through various case studies and comparative analyses, the review demonstrates the effectiveness of LSTM networks in different polymer science applications. Future directions are also discussed, with an emphasis on real-time applications and the need for interdisciplinary collaboration. The goal of this review is to connect advanced machine learning (ML) techniques with polymer science, thereby promoting innovation and improving predictive capabilities in the field.
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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.