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

• Published in: Nature communications Vol. 15; no. 1; p. 3079
• Main Authors: Liu, Mingfeng, Wang, Jiantao, Hu, Junwei, Liu, Peitao, Niu, Haiyang, Yan, Xuexi, Li, Jiangxu, Yan, Haile, Yang, Bo, Sun, Yan, Chen, Chunlin, Kresse, Georg, Zuo, Liang, Chen, Xing-Qiu
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 09.04.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 639/301/1034/1035; 639/301/119/2795; Chemical bonds; First principles; Humanities and Social Sciences; Learning algorithms; Machine learning; Metastable phases; Molecular dynamics; multidisciplinary; Phase transitions; Science; Science (multidisciplinary); Simulation; Titanium oxides
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-024-47422-1

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 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 β − λ phase transformation initiates with the formation of two-dimensional nuclei in the a b -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. Reconstructive phase transitions in materials are usually slow due to large activation energy barriers. Here, the authors show a kinetically favorable layer-by-layer mechanism in Ti 3 O 5 transformations using machine-learning molecular dynamics simulations.