ReviewInternational journal of molecular sciences2022
Machine Learning for Property Prediction and Optimization of Polymeric Nanocomposites: A State-of-the-Art.
Review in International journal of molecular sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Physics-informed AI framework for multiscale design, discovery, and optimization of polymer nanocomposite microstructures using neural operators.Scientific reports · 2026Article
- Review of Progress of AI in Biomimetics: From Biological Patterns to Closed-Loop Discovery.Biomimetics (Basel, Switzerland) · 2026Review
- Thermo-Sensitive Polymeric Networks for Next-Generation Wound Management: A Review.AAPS PharmSciTech · 2026Review
- Graphene/CNT Nanocomposites: Processing, Properties, and Applications.Nanomaterials (Basel, Switzerland) · 2026Review
- Emerging Technologies and Integrated Interdisciplinary Strategies for Mitigating Protein Aggregation in Therapeutic Formulations.Pharmaceutical research · 2026Review
- Predictive optimization of curcumin nanocomposites using hybrid machine learning and physics informed modeling.Scientific reports · 2025Article
- Monte Carlo and Machine Learning-Based Evaluation of Fe-Enriched Al Alloys for Nuclear Radiation Shielding Applications.Materials (Basel, Switzerland) · 2025Article
- Next-Generation Hydrogel Design: Computational Advances in Synthesis, Characterization, and Biomedical Applications.Polymers · 2025Review
- Machine Learning in Polymer Research.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Nanocomposites for Multifunctional Sensors: A Comprehensive Bibliometric Exploration.Nanomaterials (Basel, Switzerland) · 2024Review
- Convergence of Nanotechnology and Machine Learning: The State of the Art, Challenges, and Perspectives.International journal of molecular sciences · 2024Review
- Machine learning based predictive analysis of DNA cleavage induced by diverse nanomaterials.Scientific reports · 2024Article
- Materials science in the era of large language models: a perspective.Digital discovery · 2024Review
- Recent Trends in Polymeric Foams and Porous Structures for Electromagnetic Interference Shielding Applications.Polymers · 2024Review
- Article
- Article
- Prediction and Control of Thermal Transport at Defective State Gr/Nanomaterials (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently, the field of polymer nanocomposites has been an area of high scientific and industrial attention due to noteworthy improvements attained in these materials, arising from the synergetic combination of properties of a polymeric matrix and an organic or inorganic nanomaterial. The enhanced performance of those materials typically involves superior mechanical strength, toughness and stiffness, electrical and thermal conductivity, better flame retardancy and a higher barrier to moisture and gases. Nanocomposites can also display unique design possibilities, which provide exceptional advantages in developing multifunctional materials with desired properties for specific applications. On the other hand, machine learning (ML) has been recognized as a powerful predictive tool for data-driven multi-physical modelling, leading to unprecedented insights and an exploration of the system's properties beyond the capability of traditional computational and experimental analyses. This article aims to provide a brief overview of the most important findings related to the application of ML for the rational design of polymeric nanocomposites. Prediction, optimization, feature identification and uncertainty quantification are presented along with different ML algorithms used in the field of polymeric nanocomposites for property prediction, and selected examples are discussed. Finally, conclusions and future perspectives are highlighted.
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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.