Finite‐temperature properties of NbO2 from a deep‐learning interatomic potential
Abstract Using first‐principles‐based machine‐learning potential, molecular dynamics (MD) simulations are performed to investigate the micro‐mechanism in phase transition of NbO2. Treating the DFT results of the low‐ and intermediate‐temperature phases of NbO2 as training data in the deep‐learning m...
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| Format: | Article |
| Language: | English |
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Wiley-VCH
2025-06-01
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| Series: | Materials Genome Engineering Advances |
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| Online Access: | https://doi.org/10.1002/mgea.70011 |
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| author | Xinhang Li Yongqiang Wang Tianyu Jiao Zhaoxin Liu Chuanle Yang Ri He Liang Si |
| author_facet | Xinhang Li Yongqiang Wang Tianyu Jiao Zhaoxin Liu Chuanle Yang Ri He Liang Si |
| author_sort | Xinhang Li |
| collection | DOAJ |
| description | Abstract Using first‐principles‐based machine‐learning potential, molecular dynamics (MD) simulations are performed to investigate the micro‐mechanism in phase transition of NbO2. Treating the DFT results of the low‐ and intermediate‐temperature phases of NbO2 as training data in the deep‐learning model, we successfully constructed an interatomic potential capable of accurately reproducing the phase transitions from low‐temperature (pressure) to high‐temperature (pressure) regimes. Notably, our simulations predict a high‐pressure monoclinic phase (>14 GPa) without treating its information in the training set, consistent with previous experimental findings, demonstrating the reliability of the constructed interatomic potential. We identified the Nb‐dimers as the key structural motif governing the phase transitions. At low temperatures, the displacements of the Nb‐dimers drive the transition between the I41/a (α‐NbO2) and I41 (β‐NbO2) phases, while at high temperatures, Nb ions are prone to being equally distributed and the disappearance of Nb‐dimers leads to the stabilization of a high‐symmetry P42/mnm phase. These findings elucidate the structural and dynamical mechanisms underlying the structural properties of NbO2 and highlight the utility of combining DFT and deep potential MD methods for studying complex phase transitions in transition metal oxides. |
| format | Article |
| id | doaj-art-d8ec8682e8934c9db7bab93bbda75e64 |
| institution | DOAJ |
| issn | 2940-9489 2940-9497 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wiley-VCH |
| record_format | Article |
| series | Materials Genome Engineering Advances |
| spelling | doaj-art-d8ec8682e8934c9db7bab93bbda75e642025-08-20T03:11:42ZengWiley-VCHMaterials Genome Engineering Advances2940-94892940-94972025-06-0132n/an/a10.1002/mgea.70011Finite‐temperature properties of NbO2 from a deep‐learning interatomic potentialXinhang Li0Yongqiang Wang1Tianyu Jiao2Zhaoxin Liu3Chuanle Yang4Ri He5Liang Si6School of Physics Northwest University Xi'an ChinaSchool of Physics Northwest University Xi'an ChinaSchool of Physics Northwest University Xi'an ChinaSchool of Physics Northwest University Xi'an ChinaSchool of Physics Northwest University Xi'an ChinaKey Laboratory of Magnetic Materials Devices & Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo ChinaSchool of Physics Northwest University Xi'an ChinaAbstract Using first‐principles‐based machine‐learning potential, molecular dynamics (MD) simulations are performed to investigate the micro‐mechanism in phase transition of NbO2. Treating the DFT results of the low‐ and intermediate‐temperature phases of NbO2 as training data in the deep‐learning model, we successfully constructed an interatomic potential capable of accurately reproducing the phase transitions from low‐temperature (pressure) to high‐temperature (pressure) regimes. Notably, our simulations predict a high‐pressure monoclinic phase (>14 GPa) without treating its information in the training set, consistent with previous experimental findings, demonstrating the reliability of the constructed interatomic potential. We identified the Nb‐dimers as the key structural motif governing the phase transitions. At low temperatures, the displacements of the Nb‐dimers drive the transition between the I41/a (α‐NbO2) and I41 (β‐NbO2) phases, while at high temperatures, Nb ions are prone to being equally distributed and the disappearance of Nb‐dimers leads to the stabilization of a high‐symmetry P42/mnm phase. These findings elucidate the structural and dynamical mechanisms underlying the structural properties of NbO2 and highlight the utility of combining DFT and deep potential MD methods for studying complex phase transitions in transition metal oxides.https://doi.org/10.1002/mgea.70011deep‐learning modeldensity functional theoryinteratomic potentialmolecular dynamicsphase transitions |
| spellingShingle | Xinhang Li Yongqiang Wang Tianyu Jiao Zhaoxin Liu Chuanle Yang Ri He Liang Si Finite‐temperature properties of NbO2 from a deep‐learning interatomic potential Materials Genome Engineering Advances deep‐learning model density functional theory interatomic potential molecular dynamics phase transitions |
| title | Finite‐temperature properties of NbO2 from a deep‐learning interatomic potential |
| title_full | Finite‐temperature properties of NbO2 from a deep‐learning interatomic potential |
| title_fullStr | Finite‐temperature properties of NbO2 from a deep‐learning interatomic potential |
| title_full_unstemmed | Finite‐temperature properties of NbO2 from a deep‐learning interatomic potential |
| title_short | Finite‐temperature properties of NbO2 from a deep‐learning interatomic potential |
| title_sort | finite temperature properties of nbo2 from a deep learning interatomic potential |
| topic | deep‐learning model density functional theory interatomic potential molecular dynamics phase transitions |
| url | https://doi.org/10.1002/mgea.70011 |
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