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Layer-by-layer phase transformation in Ti3O5 revealed by machine-learning molecular dynamics simulations

Author

Listed:
  • Mingfeng Liu

    (Chinese Academy of Sciences
    University of Science and Technology of China)

  • Jiantao Wang

    (Chinese Academy of Sciences
    University of Science and Technology of China)

  • Junwei Hu

    (Northwestern Polytechnical University)

  • Peitao Liu

    (Chinese Academy of Sciences)

  • Haiyang Niu

    (Northwestern Polytechnical University)

  • Xuexi Yan

    (Chinese Academy of Sciences)

  • Jiangxu Li

    (Chinese Academy of Sciences)

  • Haile Yan

    (Northeastern University)

  • Bo Yang

    (Northeastern University)

  • Yan Sun

    (Chinese Academy of Sciences)

  • Chunlin Chen

    (Chinese Academy of Sciences)

  • Georg Kresse

    (Faculty of Physics and Center for Computational Materials Science)

  • Liang Zuo

    (Northeastern University)

  • Xing-Qiu Chen

    (Chinese Academy of Sciences)

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.

Suggested Citation

  • 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, 2024. "Layer-by-layer phase transformation in Ti3O5 revealed by machine-learning molecular dynamics simulations," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-47422-1
    DOI: 10.1038/s41467-024-47422-1
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    References listed on IDEAS

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