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  5. Unusual layer-by-layer phase reconstruction in Ti$_3$O$_5$

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Preprint
English
2023

Unusual layer-by-layer phase reconstruction in Ti$_3$O$_5$

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0 Files

English
2023
arXiv (Cornell University)
DOI: 10.48550/arxiv.2310.05683

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Kresse Georg
Kresse Georg

University of Vienna

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Mingfeng Liu
Jian-Tao Wang
Junwei Hu
+11 more

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 $\beta$- to $\lambda$-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 $\beta$-$\lambda$ 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 $\beta$-$\lambda$ transition, but also presents useful strategies and methods for tackling other complex structural phase transitions.

How to cite this publication

Mingfeng Liu, Jian-Tao Wang, Junwei Hu, Peitao Liu, Haiyang Niu, Xuexi Yan, Jiangxu Li, Haile Yan, Bo Yang, Yan Sun, Chunlin Chen, Kresse Georg, Liang Zuo, Xing‐Qiu Chen (2023). Unusual layer-by-layer phase reconstruction in Ti$_3$O$_5$. arXiv (Cornell University), DOI: 10.48550/arxiv.2310.05683.

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Publication Details

Type

Preprint

Year

2023

Authors

14

Datasets

0

Total Files

0

Language

English

Journal

arXiv (Cornell University)

DOI

10.48550/arxiv.2310.05683

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