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<p>MXenes are a versatile family of 2D inorganic materials with applications in energy storage, shielding, sensing, and catalysis. This review highlights computational studies using density functional theory and machine-learning approaches to explore their structure (stacking, functionalization, doping), properties (electronic, mechanical, magnetic), and application potential. Key advances and challenges are critically examined, offering insights into applying computational research to transition these materials from the lab to practical use.</p>

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First-principles and machine-learning approaches for interpreting and predicting the properties of MXenes

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月1日

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  • José D. Gouveia,
  • Tiago L. P. Galvão,
  • Kais Iben Nassar,
  • José R. B. Gomes

摘要

MXenes are a versatile family of 2D inorganic materials with applications in energy storage, shielding, sensing, and catalysis. This review highlights computational studies using density functional theory and machine-learning approaches to explore their structure (stacking, functionalization, doping), properties (electronic, mechanical, magnetic), and application potential. Key advances and challenges are critically examined, offering insights into applying computational research to transition these materials from the lab to practical use.