Recent years have witnessed a profound transformation in polymer science driven by the rapid integration of machine learning (ML) and artificial intelligence (AI). This review provides a comprehensive and up-to-date analysis of advances in ML applications across the polymer field from 2023 to July 2025. We examine the use of supervised, unsupervised, and reinforcement learning methods for polymer design, property prediction, structural characterization, process optimization, and sustainable materials development. Special attention is given to emerging paradigms such as high-throughput screening, inverse design, multi-scale modeling, and the use of generative models—including variational autoencoders (VAEs), graph neural networks (GNNs), and transformer-based architectures. We also explore recent innovations in explainable AI (XAI), physics-informed neural networks (PINNs), and the growing role of automated experimental platforms. Key challenges—including data scarcity, model generalization, and interpretability—are discussed alongside strategies such as transfer learning, active learning, and the development of polymer-specific representations like BigSMILES. The review concludes with future directions and the outlook for AI-powered polymer discovery, highlighting the increasing role of open-access databases, multi-modal learning, and autonomous laboratories. Together, these developments mark a paradigm shift in how polymers are conceived, characterized, and optimized—ushering in a new era of intelligent materials innovation.
| Mualliflar | Ilnar Nurgaliev, M. B. Marasulov, AKBARXON HAMZAYEV, Akmal Abilkasimov |
|---|---|
| Jurnal | O'ZBEKISTON POLIMERLAR JURNALI |
| Nashr sanasi | 2026-04-01 |
| Jild | 5 |
| Son | 1 |
| Betlar | 05-41 |
| DOI | 10.66640/ujp-2026-5-00001 |
DOI: 10.66640/ujp-2026-5-00001 · Maqolaning asl sahifasi
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A rationally engineered ternary phosphide electrocatalyst CoFeNi-P/NF was developed through hydrothermal growth followed by controlled vapor-phase phosphorization to address the kinetic limitations of alkaline water…
Dissolving polymer microneedles based on hydroxypropyl methylcellulose (HPMC), sodium carboxymethylcellulose (Na-CMC), polyvinyl alcohol (PVA), and their binary hydrogel blends were fabricated using a micromolding…
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The use of potassium permanganate (KMnO₄) as an oxidizing agent provides a relatively straightforward yet effective route for tailoring the structure of cellulose under acidic conditions. In this work, microcrystalline…
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This study presents an electrochemical impedance spectroscopy (EIS) analysis of proton exchange nanocomposite membranes (NDK) in compari-son with commercial Nafion 117 under fully hydrated conditions. The im-pedance…