Layer-by-layer phase transformation in Ti<sub>3</sub>O<sub>5</sub> revealed by machine-learning molecular dynamics simulations
- Author(s)
- 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
- 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 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.
- Organisation(s)
- Computational Materials Physics
- External organisation(s)
- Chinese Academy of Sciences (CAS), University of Science and Technology of China (USTC), Northwestern Polytechnical University, Northeastern University China
- Journal
- Nature Communications
- Volume
- 15
- No. of pages
- 10
- ISSN
- 2041-1723
- DOI
- https://doi.org/10.1038/s41467-024-47422-1
- Publication date
- 04-2024
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 103018 Materials physics, 102019 Machine learning
- ASJC Scopus subject areas
- General Chemistry, General Biochemistry,Genetics and Molecular Biology, General Physics and Astronomy
- Portal url
- https://ucrisportal.univie.ac.at/en/publications/c62c5a01-2e81-4f31-89dc-b9ad71ee3db0