Layer-by-layer phase transformation in Ti3O5 revealed by machine-learning molecular dynamics simulations
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 ba...
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| Published in | Nature communications Vol. 15; no. 1; p. 3079 |
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| Main Authors | , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
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09.04.2024
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| 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
-Ti
3
O
5
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.
Reconstructive phase transitions in materials are usually slow due to large activation energy barriers. Here, the authors show a kinetically favorable layer-by-layer mechanism in Ti
3
O
5
transformations using machine-learning molecular dynamics simulations. |
|---|---|
| AbstractList | 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 ab-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.Reconstructive phase transitions in materials are usually slow due to large activation energy barriers. Here, the authors show a kinetically favorable layer-by-layer mechanism in Ti3O5 transformations using 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 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. 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 -Ti 3 O 5 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. 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 -Ti 3 O 5 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. Reconstructive phase transitions in materials are usually slow due to large activation energy barriers. Here, the authors show a kinetically favorable layer-by-layer mechanism in Ti 3 O 5 transformations using machine-learning molecular dynamics simulations. 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 ab-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. |
| ArticleNumber | 3079 |
| Author | Yan, Haile Chen, Xing-Qiu Wang, Jiantao Yan, Xuexi Hu, Junwei Sun, Yan Niu, Haiyang Liu, Peitao Chen, Chunlin Liu, Mingfeng Li, Jiangxu Yang, Bo Kresse, Georg Zuo, Liang |
| Author_xml | – sequence: 1 givenname: Mingfeng surname: Liu fullname: Liu, Mingfeng organization: Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, School of Materials Science and Engineering, University of Science and Technology of China – sequence: 2 givenname: Jiantao surname: Wang fullname: Wang, Jiantao organization: Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, School of Materials Science and Engineering, University of Science and Technology of China – sequence: 3 givenname: Junwei orcidid: 0000-0002-6117-0623 surname: Hu fullname: Hu, Junwei organization: State Key Laboratory of Solidification Processing, International Center for Materials Discovery, School of Materials Science and Engineering, Northwestern Polytechnical University – sequence: 4 givenname: Peitao orcidid: 0000-0002-6950-1386 surname: Liu fullname: Liu, Peitao email: ptliu@imr.ac.cn organization: Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences – sequence: 5 givenname: Haiyang orcidid: 0000-0003-3049-5916 surname: Niu fullname: Niu, Haiyang email: haiyang.niu@nwpu.edu.cn organization: State Key Laboratory of Solidification Processing, International Center for Materials Discovery, School of Materials Science and Engineering, Northwestern Polytechnical University – sequence: 6 givenname: Xuexi surname: Yan fullname: Yan, Xuexi organization: Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences – sequence: 7 givenname: Jiangxu orcidid: 0000-0002-4534-4208 surname: Li fullname: Li, Jiangxu organization: Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences – sequence: 8 givenname: Haile orcidid: 0000-0003-2184-132X surname: Yan fullname: Yan, Haile organization: Key Laboratory for Anisotropy and Texture of Materials (Ministry of Education), School of Materials Science and Engineering, Northeastern University – sequence: 9 givenname: Bo surname: Yang fullname: Yang, Bo organization: Key Laboratory for Anisotropy and Texture of Materials (Ministry of Education), School of Materials Science and Engineering, Northeastern University – sequence: 10 givenname: Yan orcidid: 0000-0002-7142-8552 surname: Sun fullname: Sun, Yan organization: Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences – sequence: 11 givenname: Chunlin orcidid: 0000-0003-1985-1940 surname: Chen fullname: Chen, Chunlin organization: Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences – sequence: 12 givenname: Georg orcidid: 0000-0001-9102-4259 surname: Kresse fullname: Kresse, Georg organization: University of Vienna, Faculty of Physics and Center for Computational Materials Science – sequence: 13 givenname: Liang orcidid: 0000-0003-2971-3701 surname: Zuo fullname: Zuo, Liang organization: Key Laboratory for Anisotropy and Texture of Materials (Ministry of Education), School of Materials Science and Engineering, Northeastern University – sequence: 14 givenname: Xing-Qiu orcidid: 0000-0002-5904-6578 surname: Chen fullname: Chen, Xing-Qiu organization: Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences |
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| CitedBy_id | crossref_primary_10_1016_j_jmat_2024_06_006 |
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| Snippet | Reconstructive phase transitions involving breaking and reconstruction of primary chemical bonds are ubiquitous and important for many technological... Abstract Reconstructive phase transitions involving breaking and reconstruction of primary chemical bonds are ubiquitous and important for many technological... Abstract Reconstructive phase transitions involving breaking and reconstruction of primary chemical bonds are ubiquitous and important for many technological... |
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| SubjectTerms | 639/301/1034/1035 639/301/119/2795 Chemical bonds First principles Humanities and Social Sciences Learning algorithms Machine learning Metastable phases Molecular dynamics multidisciplinary Phase transitions Science Science (multidisciplinary) Simulation Titanium oxides |
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| Title | Layer-by-layer phase transformation in Ti3O5 revealed by machine-learning molecular dynamics simulations |
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