Boosting-Based Machine Learning Applications in Polymer Science: A Review

Malashin, Ivan; Tynchenko, Vadim; Gantimurov, Andrei; Nelyub, Vladimir; Borodulin, Aleksei

doi:10.3390/polym17040499

Open AccessReview

Boosting-Based Machine Learning Applications in Polymer Science: A Review

by

Ivan Malashin

^1,*

,

Vadim Tynchenko

^1,*

,

Andrei Gantimurov

¹

,

Vladimir Nelyub

^1,2

and

Aleksei Borodulin

¹

Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia

²

Scientific Department, Far Eastern Federal University, 690922 Vladivostok, Russia

^*

Authors to whom correspondence should be addressed.

Polymers 2025, 17(4), 499; https://doi.org/10.3390/polym17040499

Submission received: 9 January 2025 / Revised: 9 February 2025 / Accepted: 11 February 2025 / Published: 14 February 2025

(This article belongs to the Special Issue Scientific Machine Learning for Polymeric Materials)

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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.

Keywords: machine learning; boosting methods; AdaBoost; Gradient Boosting; XGBoost; CatBoost; LightGBM; polymer science

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MDPI and ACS Style

Malashin, I.; Tynchenko, V.; Gantimurov, A.; Nelyub, V.; Borodulin, A. Boosting-Based Machine Learning Applications in Polymer Science: A Review. Polymers 2025, 17, 499. https://doi.org/10.3390/polym17040499

AMA Style

Malashin I, Tynchenko V, Gantimurov A, Nelyub V, Borodulin A. Boosting-Based Machine Learning Applications in Polymer Science: A Review. Polymers. 2025; 17(4):499. https://doi.org/10.3390/polym17040499

Chicago/Turabian Style

Malashin, Ivan, Vadim Tynchenko, Andrei Gantimurov, Vladimir Nelyub, and Aleksei Borodulin. 2025. "Boosting-Based Machine Learning Applications in Polymer Science: A Review" Polymers 17, no. 4: 499. https://doi.org/10.3390/polym17040499

APA Style

Malashin, I., Tynchenko, V., Gantimurov, A., Nelyub, V., & Borodulin, A. (2025). Boosting-Based Machine Learning Applications in Polymer Science: A Review. Polymers, 17(4), 499. https://doi.org/10.3390/polym17040499

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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Boosting-Based Machine Learning Applications in Polymer Science: A Review

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