Layer-by-layer phase transformation in Ti₃O₅ 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

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

Chemistry(all), Biochemistry, Genetics and Molecular Biology(all), Physics and Astronomy(all)

Portal url

https://ucris.univie.ac.at/portal/en/publications/layerbylayer-phase-transformation-in-ti3o5-revealed-by-machinelearning-molecular-dynamics-simulations(c62c5a01-2e81-4f31-89dc-b9ad71ee3db0).html