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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2310.05683 |
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| _version_ | 1866913308011921408 |
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| author | Liu, Mingfeng Wang, Jiantao Hu, Junwei Liu, Peitao Niu, Haiyang Yan, Xuexi Li, Jiangxu Yan, Haile Yang, Bo Sun, Yan Chen, Chunlin Kresse, Georg Zuo, Liang Chen, Xing-Qiu |
| author_facet | Liu, Mingfeng Wang, Jiantao Hu, Junwei Liu, Peitao Niu, Haiyang Yan, Xuexi Li, Jiangxu Yan, Haile Yang, Bo Sun, Yan Chen, Chunlin Kresse, Georg Zuo, Liang Chen, Xing-Qiu |
| contents | 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 $ab$-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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_05683 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Layer-by-layer phase transformation in Ti$_3$O$_5$ revealed by machine learning molecular dynamics simulations Liu, Mingfeng Wang, Jiantao Hu, Junwei Liu, Peitao Niu, Haiyang Yan, Xuexi Li, Jiangxu Yan, Haile Yang, Bo Sun, Yan Chen, Chunlin Kresse, Georg Zuo, Liang Chen, Xing-Qiu Materials Science 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 $ab$-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. |
| title | Layer-by-layer phase transformation in Ti$_3$O$_5$ revealed by machine learning molecular dynamics simulations |
| topic | Materials Science |
| url | https://arxiv.org/abs/2310.05683 |