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

被引：0

作者：

Gouveia, Jose D. ^{[1
]}

Galvao, Tiago L. P. ^{[2
]}

Nassar, Kais Iben ^{[1
]}

Gomes, Jose R. B. ^{[1
]}

机构：

[1] Univ Aveiro, Campus Univ Santiago, CICECO Aveiro Inst Mat, Dept Chem, Aveiro, Portugal

[2] Univ Aveiro, Campus Univ Santiago, CICECO Aveiro Inst Mat, Dept Mat & Ceram Engn, Aveiro, Portugal

来源：

NPJ 2D MATERIALS AND APPLICATIONS | 2025年 / 9卷 / 01期

关键词：

GENERALIZED-GRADIENT-APPROXIMATION; DENSITY-FUNCTIONAL APPROXIMATIONS; HYDROGEN EVOLUTION REACTION; TRANSITION-METAL CARBIDES; MAGNETIC-PROPERTIES; CATALYTIC-ACTIVITY; AMMONIA-SYNTHESIS; SURFACE-STRUCTURE; OXYGEN REDUCTION; CO2; REDUCTION;

D O I：

10.1038/s41699-025-00529-5

中图分类号：

TB3 [工程材料学];

学科分类号：

0805 ; 080502 ;

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.

引用

页数：31

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