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