Machine learning and AI in polymer science: a review of progress, challenges, and future directions

Ilnar Nurgaliev, M. B. Marasulov, AKBARXON HAMZAYEV, Akmal Abilkasimov

O'ZBEKISTON POLIMERLAR JURNALI · 2026-yil

Annotatsiya

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.

Maqola ma’lumotlari
MualliflarIlnar Nurgaliev, M. B. Marasulov, AKBARXON HAMZAYEV, Akmal Abilkasimov
JurnalO'ZBEKISTON POLIMERLAR JURNALI
NashriyotПолимерлар кимёси ва физикаси институти, www.polchemphys.uz
ISSN2181-3256, 2181-3248
Nashr sanasi2026-04-01
Jild5
Son1
Betlar05-41
TilIngliz
Ilmiy sohaKimyo
DOI10.66640/ujp-2026-5-00001
Indeksga qo‘shilgan2026-09-02

DOI: 10.66640/ujp-2026-5-00001 · Maqolaning asl sahifasi

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Kimyo

Iqtibos keltirish

APAIlnar Nurgaliev, M. B. Marasulov, AKBARXON HAMZAYEV, & Akmal Abilkasimov (2026). MACHINE LEARNING AND AI IN POLYMER SCIENCE: A REVIEW OF PROGRESS, CHALLENGES, AND FUTURE DIRECTIONS. O'ZBEKISTON POLIMERLAR JURNALI, 5(1), 05-41. https://doi.org/10.66640/ujp-2026-5-00001
GOST (OAK)Ilnar Nurgaliev va b. MACHINE LEARNING AND AI IN POLYMER SCIENCE: A REVIEW OF PROGRESS, CHALLENGES, AND FUTURE DIRECTIONS // O'ZBEKISTON POLIMERLAR JURNALI. – 2026. – T. 5, № 1. – P. 05-41. – DOI: 10.66640/ujp-2026-5-00001.
MLAIlnar Nurgaliev, M. B. Marasulov, AKBARXON HAMZAYEV, and Akmal Abilkasimov. “MACHINE LEARNING AND AI IN POLYMER SCIENCE: A REVIEW OF PROGRESS, CHALLENGES, AND FUTURE DIRECTIONS.” O'ZBEKISTON POLIMERLAR JURNALI, 5(1), 05-41, 2026. https://doi.org/10.66640/ujp-2026-5-00001
ChicagoIlnar Nurgaliev, M. B. Marasulov, AKBARXON HAMZAYEV, and Akmal Abilkasimov. “MACHINE LEARNING AND AI IN POLYMER SCIENCE: A REVIEW OF PROGRESS, CHALLENGES, AND FUTURE DIRECTIONS.” O'ZBEKISTON POLIMERLAR JURNALI, 5(1), 05-41 (2026). https://doi.org/10.66640/ujp-2026-5-00001
HarvardIlnar Nurgaliev, M. B. Marasulov, AKBARXON HAMZAYEV, and Akmal Abilkasimov (2026) ‘MACHINE LEARNING AND AI IN POLYMER SCIENCE: A REVIEW OF PROGRESS, CHALLENGES, AND FUTURE DIRECTIONS’, O'ZBEKISTON POLIMERLAR JURNALI, 5(1), 05-41. https://doi.org/10.66640/ujp-2026-5-00001
BibTeX
@article{ilnarnurgaliev2026,
  title = {MACHINE LEARNING AND AI IN POLYMER SCIENCE: A REVIEW OF PROGRESS, CHALLENGES, AND FUTURE DIRECTIONS},
  author = {Ilnar Nurgaliev and M. B. Marasulov and AKBARXON HAMZAYEV and Akmal Abilkasimov},
  journal = {O'ZBEKISTON POLIMERLAR JURNALI},
  year = {2026},
  volume = {5},
  number = {1},
  pages = {05-41},
  doi = {10.66640/ujp-2026-5-00001},
  url = {https://doi.org/10.66640/ujp-2026-5-00001}
}

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