Layer-by-layer phase transformation in Ti$_3$O$_5$ revealed by machine learning molecular dynamics simulations
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, and Xing-Qiu Chen

TL;DR
This study uncovers a layer-by-layer phase transformation mechanism in Ti3O5 using machine learning-enhanced molecular dynamics, revealing how ultrafast, reversible transitions occur at the microscopic level.
Contribution
It introduces a new in-plane nucleated layer-by-layer transformation mechanism for Ti3O5 and develops an efficient machine learning potential for large-scale simulations.
Findings
Layer-by-layer transformation initiates with 2D nuclei formation.
Transformation proceeds via metastable intermediate phases.
Machine learning potential enables near first-principles accuracy simulations.
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 - to -TiO 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…
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Taxonomy
TopicsMachine Learning in Materials Science · Electronic and Structural Properties of Oxides · Innovative Microfluidic and Catalytic Techniques Innovation
