Layer-by-layer phase transformation in Ti3O5 revealed by machine-learning molecular dynamics simulations
Abstract Reconstructive phase transitions involving breaking and reconstruction of primary chemical bonds are ubiquitous and important for many technological applications. In contrast to displacive phase transitions, the dynamics of reconstructive phase transitions are usually slow due to the large...
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Nature Portfolio
2024-04-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-47422-1 |
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| author | Mingfeng Liu Jiantao Wang Junwei Hu Peitao Liu Haiyang Niu Xuexi Yan Jiangxu Li Haile Yan Bo Yang Yan Sun Chunlin Chen Georg Kresse Liang Zuo Xing-Qiu Chen |
| author_facet | Mingfeng Liu Jiantao Wang Junwei Hu Peitao Liu Haiyang Niu Xuexi Yan Jiangxu Li Haile Yan Bo Yang Yan Sun Chunlin Chen Georg Kresse Liang Zuo Xing-Qiu Chen |
| author_sort | Mingfeng Liu |
| collection | DOAJ |
| description | Abstract Reconstructive phase transitions involving breaking and reconstruction of primary chemical bonds are ubiquitous and important for many technological applications. In contrast to displacive phase transitions, the dynamics of reconstructive phase transitions are usually slow due to the large energy barrier. Nevertheless, the reconstructive phase transformation from β- to λ-Ti3O5 exhibits an ultrafast and reversible behavior. Despite extensive studies, the underlying microscopic mechanism remains unclear. Here, we discover a kinetically favorable in-plane nucleated layer-by-layer transformation mechanism through metadynamics and large-scale molecular dynamics simulations. This is enabled by developing an efficient machine learning potential with near first-principles accuracy through an on-the-fly active learning method and an advanced sampling technique. Our results reveal that the β−λ phase transformation initiates with the formation of two-dimensional nuclei in the a b-plane and then proceeds layer-by-layer through a multistep barrier-lowering kinetic process via intermediate metastable phases. Our work not only provides important insight into the ultrafast and reversible nature of the β−λ transition, but also presents useful strategies and methods for tackling other complex structural phase transitions. |
| format | Article |
| id | doaj-art-28da3602c2f24ba58ca64983b8fcddec |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2024-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-28da3602c2f24ba58ca64983b8fcddec2025-01-26T12:40:07ZengNature PortfolioNature Communications2041-17232024-04-0115111010.1038/s41467-024-47422-1Layer-by-layer phase transformation in Ti3O5 revealed by machine-learning molecular dynamics simulationsMingfeng Liu0Jiantao Wang1Junwei Hu2Peitao Liu3Haiyang Niu4Xuexi Yan5Jiangxu Li6Haile Yan7Bo Yang8Yan Sun9Chunlin Chen10Georg Kresse11Liang Zuo12Xing-Qiu Chen13Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of SciencesShenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of SciencesState Key Laboratory of Solidification Processing, International Center for Materials Discovery, School of Materials Science and Engineering, Northwestern Polytechnical UniversityShenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of SciencesState Key Laboratory of Solidification Processing, International Center for Materials Discovery, School of Materials Science and Engineering, Northwestern Polytechnical UniversityShenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of SciencesShenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of SciencesKey Laboratory for Anisotropy and Texture of Materials (Ministry of Education), School of Materials Science and Engineering, Northeastern UniversityKey Laboratory for Anisotropy and Texture of Materials (Ministry of Education), School of Materials Science and Engineering, Northeastern UniversityShenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of SciencesShenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of SciencesUniversity of Vienna, Faculty of Physics and Center for Computational Materials ScienceKey Laboratory for Anisotropy and Texture of Materials (Ministry of Education), School of Materials Science and Engineering, Northeastern UniversityShenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of SciencesAbstract Reconstructive phase transitions involving breaking and reconstruction of primary chemical bonds are ubiquitous and important for many technological applications. In contrast to displacive phase transitions, the dynamics of reconstructive phase transitions are usually slow due to the large energy barrier. Nevertheless, the reconstructive phase transformation from β- to λ-Ti3O5 exhibits an ultrafast and reversible behavior. Despite extensive studies, the underlying microscopic mechanism remains unclear. Here, we discover a kinetically favorable in-plane nucleated layer-by-layer transformation mechanism through metadynamics and large-scale molecular dynamics simulations. This is enabled by developing an efficient machine learning potential with near first-principles accuracy through an on-the-fly active learning method and an advanced sampling technique. Our results reveal that the β−λ phase transformation initiates with the formation of two-dimensional nuclei in the a b-plane and then proceeds layer-by-layer through a multistep barrier-lowering kinetic process via intermediate metastable phases. Our work not only provides important insight into the ultrafast and reversible nature of the β−λ transition, but also presents useful strategies and methods for tackling other complex structural phase transitions.https://doi.org/10.1038/s41467-024-47422-1 |
| spellingShingle | Mingfeng Liu Jiantao Wang Junwei Hu Peitao Liu Haiyang Niu Xuexi Yan Jiangxu Li Haile Yan Bo Yang Yan Sun Chunlin Chen Georg Kresse Liang Zuo Xing-Qiu Chen Layer-by-layer phase transformation in Ti3O5 revealed by machine-learning molecular dynamics simulations Nature Communications |
| title | Layer-by-layer phase transformation in Ti3O5 revealed by machine-learning molecular dynamics simulations |
| title_full | Layer-by-layer phase transformation in Ti3O5 revealed by machine-learning molecular dynamics simulations |
| title_fullStr | Layer-by-layer phase transformation in Ti3O5 revealed by machine-learning molecular dynamics simulations |
| title_full_unstemmed | Layer-by-layer phase transformation in Ti3O5 revealed by machine-learning molecular dynamics simulations |
| title_short | Layer-by-layer phase transformation in Ti3O5 revealed by machine-learning molecular dynamics simulations |
| title_sort | layer by layer phase transformation in ti3o5 revealed by machine learning molecular dynamics simulations |
| url | https://doi.org/10.1038/s41467-024-47422-1 |
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