First-principles and machine-learning approaches for interpreting and predicting the properties of MXenes
Abstract 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, functionalizatio...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | npj 2D Materials and Applications |
| Online Access: | https://doi.org/10.1038/s41699-025-00529-5 |
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| _version_ | 1832571648811728896 |
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| author | José D. Gouveia Tiago L. P. Galvão Kais Iben Nassar José R. B. Gomes |
| author_facet | José D. Gouveia Tiago L. P. Galvão Kais Iben Nassar José R. B. Gomes |
| author_sort | José D. Gouveia |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-45d48a888ed34315ad9a91edbc1cfbb4 |
| institution | Kabale University |
| issn | 2397-7132 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj 2D Materials and Applications |
| spelling | doaj-art-45d48a888ed34315ad9a91edbc1cfbb42025-02-02T12:27:08ZengNature Portfolionpj 2D Materials and Applications2397-71322025-02-019113110.1038/s41699-025-00529-5First-principles and machine-learning approaches for interpreting and predicting the properties of MXenesJosé D. Gouveia0Tiago L. P. Galvão1Kais Iben Nassar2José R. B. Gomes3Department of Chemistry, CICECO – Aveiro Institute of Materials, University of Aveiro, Campus Universitário de SantiagoDepartment of Materials and Ceramic Engineering, CICECO – Aveiro Institute of Materials, University of Aveiro, Campus Universitário de SantiagoDepartment of Chemistry, CICECO – Aveiro Institute of Materials, University of Aveiro, Campus Universitário de SantiagoDepartment of Chemistry, CICECO – Aveiro Institute of Materials, University of Aveiro, Campus Universitário de SantiagoAbstract 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.https://doi.org/10.1038/s41699-025-00529-5 |
| spellingShingle | José D. Gouveia Tiago L. P. Galvão Kais Iben Nassar José R. B. Gomes First-principles and machine-learning approaches for interpreting and predicting the properties of MXenes npj 2D Materials and Applications |
| title | First-principles and machine-learning approaches for interpreting and predicting the properties of MXenes |
| title_full | First-principles and machine-learning approaches for interpreting and predicting the properties of MXenes |
| title_fullStr | First-principles and machine-learning approaches for interpreting and predicting the properties of MXenes |
| title_full_unstemmed | First-principles and machine-learning approaches for interpreting and predicting the properties of MXenes |
| title_short | First-principles and machine-learning approaches for interpreting and predicting the properties of MXenes |
| title_sort | first principles and machine learning approaches for interpreting and predicting the properties of mxenes |
| url | https://doi.org/10.1038/s41699-025-00529-5 |
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