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An investigation of self-interstitial diffusion in α-zirconium by an on-the-fly machine learning force field

文献类型：期刊论文

作者Shi, Tan7; Liu, Wenlong6; Zhang, Chen7; Lyu, Sixin7; Sun, Zhipeng5; Peng, Qing2,3,4; Li, Yuanming5; Meng, Fanqiang6; Tang, Chuanbao5; Lu, Chenyang1,7
刊名AIP ADVANCES
出版日期2024-05-01
卷号14期号:5页码:7
DOI10.1063/5.0211883
通讯作者Meng, Fanqiang(mengfq5@mail.sysu.edu.cn) ; Tang, Chuanbao(383164381@qq.com) ; Lu, Chenyang(chenylu@xjtu.edu.cn)
英文摘要The on-the-fly machine learning force field approach, based on the Gaussian approximation potential and Bayesian error estimation, was used to study the diffusion of self-interstitial atoms in alpha-zirconium. Ab initio molecular dynamics simulations of lattice vibration and interstitial diffusion at different temperatures were employed to develop the force field. The radial and angular descriptors of the potential were further optimized to achieve better agreement with first-principles results. Subsequent long-term diffusion simulations were performed to assess the diffusion behavior based on the obtained force field. Tracer diffusion coefficients and diffusion anisotropy were studied at temperatures of 600-1200 K, and the Bayesian errors were estimated throughout the diffusion simulations. The mean and maximum estimated Bayesian errors of atomic force were approximately twice as large as those observed during the learning period. The basal diffusion was greatly favored compared to the interstitial diffusion along the c-axis, consistent with previous simulations based on first-principles results and classical potentials. The accuracy and applicability of the current on-the-fly machine learning approach were critically evaluated.
WOS关键词POINT-DEFECT DIFFUSION ; APPROXIMATION ; SIMULATIONS ; ANISOTROPY ; GROWTH
资助项目National Key Research and Development Program of Chinahttps://doi.org/10.13039/501100012166[2022YFB1902402] ; National Key Research and Development Program of China[E1Z1011001] ; Institute of Mechanics, Chinese Academy of Sciences
WOS研究方向Science & Technology - Other Topics ; Materials Science ; Physics
语种英语
WOS记录号WOS:001225950900007
资助机构National Key Research and Development Program of Chinahttps://doi.org/10.13039/501100012166 ; National Key Research and Development Program of China ; Institute of Mechanics, Chinese Academy of Sciences
源URL[http://dspace.imech.ac.cn/handle/311007/95429]  
专题力学研究所_非线性力学国家重点实验室
通讯作者Meng, Fanqiang; Tang, Chuanbao; Lu, Chenyang
作者单位1.Xi An Jiao Tong Univ, State Key Lab Multiphase Flow Power Engn, Xian 710049, Peoples R China
2.Guangdong Aerosp Res Acad, Guangzhou 511458, Peoples R China
3.Univ Chinese Acad Sci, Ctr Mat Sci & Optoelect Engn, Beijing 100049, Peoples R China
4.Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100190, Peoples R China
5.Nucl Power Inst China, Chengdu 610213, Peoples R China
6.Sun Yat sen Univ, Sino French Inst Nucl Engn & Technol, Zhuhai 519082, Peoples R China
7.Xi An Jiao Tong Univ, Sch Nucl Sci & Technol, Xian 710049, Peoples R China
推荐引用方式
GB/T 7714		 Shi, Tan,Liu, Wenlong,Zhang, Chen,et al. An investigation of self-interstitial diffusion in α-zirconium by an on-the-fly machine learning force field[J]. AIP ADVANCES,2024,14(5):7.
APA Shi, Tan.,Liu, Wenlong.,Zhang, Chen.,Lyu, Sixin.,Sun, Zhipeng.,...&Lu, Chenyang.(2024).An investigation of self-interstitial diffusion in α-zirconium by an on-the-fly machine learning force field.AIP ADVANCES,14(5),7.
MLA Shi, Tan,et al."An investigation of self-interstitial diffusion in α-zirconium by an on-the-fly machine learning force field".AIP ADVANCES 14.5(2024):7.

入库方式： OAI收割

来源：力学研究所

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