Layer-by-layer phase transformation in Ti_3O_5 revealed by machine learning molecular dynamics simulations
arxiv(2023)
摘要
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-Ti_3O_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 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.
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