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Quantitative Topic Analysis of Materials Science Literature Using Natural Language Processing.

Jaewoong Choi1, Byungju Lee1

  • 1Computational Science Research Center, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea.

ACS Applied Materials & Interfaces
|December 7, 2023
PubMed
Summary

This study uses natural language processing to map 257 materials science topics, revealing research trends and national interests. It highlights emerging interdisciplinary fields like machine learning in materials and wearable electronics for future potential.

Keywords:
Literature miningMaterials scienceNatural language processingResearch interestResearch trendsTopic analysisUnsupervised learning

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Area of Science:

  • Materials Science
  • Computational Materials Science

Background:

  • Materials science research is rapidly expanding, making it difficult to understand the evolving landscape and identify strategic insights.
  • The increasing volume and diversity of scientific publications pose challenges for researchers, policymakers, and industry stakeholders.

Purpose of the Study:

  • To develop a natural language processing (NLP) protocol for extracting text-encoded topics from large-scale scientific literature.
  • To uncover research interests, convergence trends, and the strategic landscape of materials science.
  • To provide a framework for understanding current and future materials science research directions.

Main Methods:

  • Utilized a natural language processing (NLP) based protocol to analyze a large corpus of scientific literature.
  • Generated a topic map identifying 257 distinct materials science topics, including biocompatible materials, structural materials, electrochemistry, and photonics.
  • Constructed a topic association network to analyze interdisciplinary research and identify high-centrality fields.

Main Results:

  • A comprehensive topic map of the materials science research landscape was created, detailing 257 topics.
  • Analysis revealed national research interests and competitive strategies, with the US showing early adoption of machine learning in materials science.
  • Journal-level analyses indicated shifts in focus over time, and the topic association network highlighted key interdisciplinary areas like machine learning-enabled composite modeling, energy policy, and wearable electronics.

Conclusions:

  • The developed NLP protocol effectively maps the materials science research landscape and identifies emerging trends.
  • The study provides valuable insights into national research strategies and the future potential of interdisciplinary research in materials science.
  • This work facilitates a better understanding of the dynamic and diverse field of materials science for all stakeholders.