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

共 50 条

[41] Machine Learning Accelerated First-Principles Study of the Hydrodeoxygenation of Propanoic Acid
Yang, Wenqiang
Abdelfatah, Kareem E.
Kundu, Subrata Kumar
Rajbanshi, Biplab
Terejanu, Gabriel A.
Heyden, Andreas
ACS CATALYSIS, 2024, 14 (13): : 10148 - 10163
[42] Machine learning-aided first-principles calculations of redox potentials
Jinnouchi, Ryosuke
Karsai, Ferenc
Kresse, Georg
NPJ COMPUTATIONAL MATERIALS, 2024, 10 (01)
[43] First-Principles Performance Prediction of High Explosives Enabled by Machine Learning
Lindquist, Beth A.
Jadrich, Ryan B.
Leiding, Jeffery A.
JOURNAL OF PHYSICAL CHEMISTRY C, 2024, 128 (09): : 3945 - 3954
[44] Incorporating Unmodeled Dynamics Into First-Principles Models Through Machine Learning
Quaghebeur, Ward
Nopens, Ingmar
De Baets, Bernard
IEEE ACCESS, 2021, 9 : 22014 - 22022
[45] Approaches to atomistic triple-line properties from first-principles
Hashibon, Adham
Elsaesser, Christian
SCRIPTA MATERIALIA, 2010, 62 (12) : 939 - 944
[46] Rapid and accurate identification of structural and electronic properties of Xenes under different strains via a first-principles machine-learning approach for energy-electronic devices
Wang, Guoqing
Liu, Rongchao
Gebreslassie, Gebrehiwot
Desta, Halefom G.
Tian, Dong
Lin, Bin
MATERIALS TODAY COMMUNICATIONS, 2024, 40
[47] First-principles and Monte Carlo simulations of high-entropy MXenes
Oyeniran, Noah
Chowdhury, Oyshee
Hu, Chongze
APPLIED PHYSICS LETTERS, 2025, 126 (12)
[48] Ionic liquids intercalation in titanium carbide MXenes: A first-principles investigation
Zhang, Shaoze
Jiang, De-en
Zhou, Nan
Tang, Jiaxing
Zhang, Keyu
Li, Yin
Hu, Junxian
Peng, Changjun
Liu, Honglai
Yang, Bin
Yao, Yaochun
JOURNAL OF COMPUTATIONAL CHEMISTRY, 2024, 45 (27) : 2294 - 2307
[49] Mechanical properties of Mo-Re alloy based on first-principles and machine learning potential function
Yang, Wu
Ye, Jingwen
Bi, Peng
Huang, Baosheng
Chen, Liang
Yi, Yong
MATERIALS TODAY COMMUNICATIONS, 2024, 38
[50] Accelerating Pmn21-BAlNP properties prediction by machine learning based on first-principles calculation
Zhu, Chuanshuai
Yang, Ruike
Chai, Bao
Wei, Qun
Zhang, Dongyun
JOURNAL OF PHYSICS AND CHEMISTRY OF SOLIDS, 2019, 126 : 224 - 233

← 1 2 3 4 5 →