Layer-by-layer phase transformation in Ti<sub>3</sub>O<sub>5</sub> revealed by machine-learning molecular dynamics simulations

doi:10.1038/s41467-024-47422-1

IMR OpenIR

	Layer-by-layer phase transformation in Ti₃O₅ revealed by machine-learning molecular dynamics simulations
	Liu, Mingfeng 1,2; Wang, Jiantao 1,2; Hu, Junwei 3; Liu, Peitao 1; Niu, Haiyang 3; Yan, Xuexi 1; Li, Jiangxu 1; Yan, Haile 4; Yang, Bo 4; Sun, Yan 1; Chen, Chunlin 1; Kresse, Georg 5; Zuo, Liang 4; Chen, Xing-Qiu 1
通讯作者	Liu, Peitao(ptliu@imr.ac.cn) ; Niu, Haiyang(haiyang.niu@nwpu.edu.cn)
	2024-04-09
发表期刊	NATURE COMMUNICATIONS
卷号	15 期号:1 页码:10
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 beta- to lambda-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 beta-lambda 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 beta-lambda transition, but also presents useful strategies and methods for tackling other complex structural phase transitions.
资助者	The work at the Institute of Metal Research is supported by the National Natural Science Foundation of China (Grant No. 52201030 and Grant No. 52188101), the National Key RD Program of China 2021YFB3501503, Chinese Academy of Sciences (Grant No. ZDRW-CN-2 ; National Natural Science Foundation of China ; National Key R& D Program of China ; Chinese Academy of Sciences ; National Science Fund for Distinguished Young Scholars ; Research Fund of the State Key Laboratory of Solidification Processing (NPU), China ; Austrian Science Fund (FWF) within the SFB TACO
DOI	10.1038/s41467-024-47422-1
收录类别	SCI
语种	英语
资助项目	The work at the Institute of Metal Research is supported by the National Natural Science Foundation of China (Grant No. 52201030 and Grant No. 52188101), the National Key RD Program of China 2021YFB3501503, Chinese Academy of Sciences (Grant No. ZDRW-CN-2[22003050] ; National Natural Science Foundation of China[2021YFB3501503] ; National Key R& D Program of China[ZDRW-CN-2021-2-5] ; Chinese Academy of Sciences[51725103] ; National Science Fund for Distinguished Young Scholars[2020-QZ-03] ; Research Fund of the State Key Laboratory of Solidification Processing (NPU), China[F 81-N] ; Austrian Science Fund (FWF) within the SFB TACO
WOS研究方向	Science & Technology - Other Topics
WOS类目	Multidisciplinary Sciences
WOS记录号	WOS:001202403000026
出版者	NATURE PORTFOLIO
引用统计	被引频次：10[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.imr.ac.cn/handle/321006/185709
专题	中国科学院金属研究所
通讯作者	Liu, Peitao; Niu, Haiyang
作者单位	1.Chinese Acad Sci, Inst Met Res, Shenyang Natl Lab Mat Sci, Shenyang 110016, Peoples R China 2.Univ Sci & Technol China, Sch Mat Sci & Engn, Shenyang 110016, Peoples R China 3.Northwestern Polytech Univ, Int Ctr Mat Discovery, Sch Mat Sci & Engn, State Key Lab Solidificat Proc, Xian 710072, Peoples R China 4.Northeastern Univ, Sch Mat Sci & Engn, Key Lab Anisotropy & Texture Mat, Minist Educ, Shenyang 110819, Peoples R China 5.Univ Vienna, Fac Phys, Ctr Computat Mat Sci, Kolingasse 14-16, A-1090 Vienna, Austria
推荐引用方式 GB/T 7714	Liu, Mingfeng,Wang, Jiantao,Hu, Junwei,et al. Layer-by-layer phase transformation in Ti₃O₅ revealed by machine-learning molecular dynamics simulations[J]. NATURE COMMUNICATIONS,2024,15(1):10.
APA	Liu, Mingfeng.,Wang, Jiantao.,Hu, Junwei.,Liu, Peitao.,Niu, Haiyang.,...&Chen, Xing-Qiu.(2024).Layer-by-layer phase transformation in Ti₃O₅ revealed by machine-learning molecular dynamics simulations.NATURE COMMUNICATIONS,15(1),10.
MLA	Liu, Mingfeng,et al."Layer-by-layer phase transformation in Ti₃O₅ revealed by machine-learning molecular dynamics simulations".NATURE COMMUNICATIONS 15.1(2024):10.