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Related Experiment Video

Updated: Jan 20, 2026

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Historical Data Mining Deep Dive into Machine Learning-Aided 2D Materials Research in Electrochemical Applications.

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Summary

Machine learning accelerates the discovery and optimization of 2D materials for energy applications. This review analyzes trends and highlights how machine learning, density functional theory, and experiments advance materials science.

Keywords:
2D materialsKDDconversiondata miningdata scienceelectrochemistryenergymachine learningstorage

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Electrochemistry

Background:

  • Machine learning (ML) is revolutionizing 2D materials design.
  • ML accelerates discovery, optimization, and screening processes.
  • Integration of ML in 2D materials for electrochemical energy applications is a growing field.

Purpose of the Study:

  • To review the historical and ongoing integration of ML in 2D materials for electrochemical energy applications.
  • To analyze trends and insights using the Knowledge Discovery in Databases (KDD) approach.
  • To highlight the synergy between ML, density functional theory (DFT), and experimentation.

Main Methods:

  • Data mining from the Scopus database.
  • Analysis of citations, keywords, and trends.
  • Computer analysis of literature reports (heat maps, network graphs) for macro and micro scope insights.

Main Results:

  • Identified key insights from large-scale literature analysis.
  • Showcased ML techniques for identifying critical material properties.
  • Demonstrated the joint advancement of materials science through ML, DFT, and experimentation.

Conclusions:

  • ML, DFT, and traditional experimentation are collectively driving progress in 2D materials for energy applications.
  • This review provides a comprehensive analysis of ML applications in batteries, fuel cells, supercapacitors, and synthesis.
  • ML is crucial for enhancing the identification of essential material properties.