The Role of Artificial Intelligence and Machine Learning in Polymer Characterization: Emerging Trends and Perspectives

The application of artificial intelligence (AI) and machine learning (ML) is rapidly expanding and has begun to make a significant impact on polymer development and characterization. This perspective article explores the current state of AI in this field and highlights areas where its potential remains underutilized. While the optimization of polymer synthesis to achieve desired properties and the classification of polymer types are well-established, opportunities for AI integration in detailed characterization, analytical method development, and data processing remain largely untapped. Greater automation of the analytical laboratory, whether through dedicated algorithms or AI-driven solutions, will enable analytical chemists to focus more on addressing research questions and interpreting results, rather than on method development and routine measurements.

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Publication Details

Journal
Chromatographia
Published
2025-04-04
DOI
https://doi.org/10.1007/s10337-025-04406-7
Citations
3
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
1.55

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article

The Role of Artificial Intelligence and Machine Learning in Polymer Characterization: Emerging Trends and Perspectives

Tijmen S. Bos, Rick S. van den Hurk, Bob W.J. Pirok
3 citations
Chromatographia
Machine Learning in Materials Science
1.55
article

The Role of Artificial Intelligence and Machine Learning in Polymer Characterization: Emerging Trends and Perspectives

Tijmen S. Bos, Rick S. van den Hurk, Bob W.J. Pirok
article en
3 citations

Abstract

The application of artificial intelligence (AI) and machine learning (ML) is rapidly expanding and has begun to make a significant impact on polymer development and characterization. This perspective article explores the current state of AI in this field and highlights areas where its potential remains underutilized. While the optimization of polymer synthesis to achieve desired properties and the classification of polymer types are well-established, opportunities for AI integration in detailed characterization, analytical method development, and data processing remain largely untapped. Greater automation of the analytical laboratory, whether through dedicated algorithms or AI-driven solutions, will enable analytical chemists to focus more on addressing research questions and interpreting results, rather than on method development and routine measurements.

ChromatographiaVol. 88(5)
University of Amsterdam (NL)
Nederlandse Organisatie voor Wetenschappelijk Onderzoek
Industry, innovation and infrastructure
Openalex Percentile: Top 19%
Machine Learning in Materials Science
1.55
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The Role of Artificial Intelligence and Machine Learning in Polymer Characterization: Emerging Trends and Perspectives — Tijmen S. Bos, Rick S. van den Hurk, et al. · Chromatographia (2025) | TGRS Research Map | TGRS