Machine learning assisted High-Throughput study of M$_4$X$_3$T$_x$ MXenes
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866912942639808512 |
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| author | Goel, Sakshi Kashyap, Arti |
| author_facet | Goel, Sakshi Kashyap, Arti |
| contents | In this work, we employ a machine-learning-assisted high-throughput density functional theory framework to systematically investigate the stability, electronic structure, and magnetic ground states of 234 M$_4$X$_3$T$_x$ MXenes. The machine learning model predicts lattice parameters with up to 94% accuracy using a relatively small training dataset and significantly reduces structural optimization time in high-throughput calculations. Based on total energy and density-of-states analyses, we classify the magnetic nature of MXenes across different transition- metal compositions and surface terminations. Ti-, Zr-, Hf-, Nb-, and Ta-based MXenes are found to be non-magnetic metals for all functional groups considered, while Sc- and Y-based systems exhibit a range of behaviors including weak ferromagnetism and semiconducting character. V- and Fe-based MXenes are identified as antiferromagnetic metals, whereas Cr- and Mn-based MXenes yield 16 ferromagnetic systems with spin polarization ranging from 50% to 100%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_04103 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Machine learning assisted High-Throughput study of M$_4$X$_3$T$_x$ MXenes Goel, Sakshi Kashyap, Arti Materials Science In this work, we employ a machine-learning-assisted high-throughput density functional theory framework to systematically investigate the stability, electronic structure, and magnetic ground states of 234 M$_4$X$_3$T$_x$ MXenes. The machine learning model predicts lattice parameters with up to 94% accuracy using a relatively small training dataset and significantly reduces structural optimization time in high-throughput calculations. Based on total energy and density-of-states analyses, we classify the magnetic nature of MXenes across different transition- metal compositions and surface terminations. Ti-, Zr-, Hf-, Nb-, and Ta-based MXenes are found to be non-magnetic metals for all functional groups considered, while Sc- and Y-based systems exhibit a range of behaviors including weak ferromagnetism and semiconducting character. V- and Fe-based MXenes are identified as antiferromagnetic metals, whereas Cr- and Mn-based MXenes yield 16 ferromagnetic systems with spin polarization ranging from 50% to 100%. |
| title | Machine learning assisted High-Throughput study of M$_4$X$_3$T$_x$ MXenes |
| topic | Materials Science |
| url | https://arxiv.org/abs/2603.04103 |