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Video Experimental Relacionado

Updated: Jan 20, 2026

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Minería de Datos Histórica Profundiza en la Investigación de Materiales 2D Asistida por Aprendizaje Automático en

Krittapong Deshsorn^1,2, Panwad Chavalekvirat^1,2, Somrudee Deepaisarn^3,2

¹School of Bio-Chemical Engineering and Technology, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12120, Thailand.

ACS materials Au

|January 19, 2026

Resumen

El aprendizaje automático acelera el descubrimiento y la optimización de materiales 2D para aplicaciones energéticas. Esta revisión analiza las tendencias y destaca cómo el aprendizaje automático, la teoría de la funcional de la densidad y la experimentación avanzan la ciencia de los materiales.

Palabras clave:

materiales 2D KDD conversión minería de datos ciencia de datos electroquímica energía aprendizaje automático almacenamiento

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Área de la Ciencia:

Materials Science
Computational Chemistry
Electrochemistry

Sus antecedentes:

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.

Objetivo del estudio:

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.

Principales métodos:

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.

Principales resultados:

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.

Conclusiones:

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.