| An investigation of self-interstitial diffusion in α-zirconium by an on-the-fly machine learning force field | |
Shi, Tan1; Liu, Wenlong2; Zhang, Chen1; Lyu, Sixin1; Sun, Zhipeng3; Peng, Qing4,5,6 ; Li, Yuanming3; Meng, Fanqiang2; Tang, Chuanbao3; Lu, Chenyang1,7
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| Corresponding Author | Meng, Fanqiang(mengfq5@mail.sysu.edu.cn) ; Tang, Chuanbao(383164381@qq.com) ; Lu, Chenyang(chenylu@xjtu.edu.cn) |
| Source Publication | AIP ADVANCES
 |
| 2024-05-01 | |
| Volume | 14Issue:5Pages:7 |
| Abstract | 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. |
| DOI | 10.1063/5.0211883 |
| Indexed By | SCI |
| Language | 英语 |
| WOS ID | WOS:001225950900007 |
| WOS Keyword | POINT-DEFECT DIFFUSION ; APPROXIMATION ; SIMULATIONS ; ANISOTROPY ; GROWTH |
| WOS Research Area | Science & Technology - Other Topics ; Materials Science ; Physics |
| WOS Subject | Nanoscience & Nanotechnology ; Materials Science, Multidisciplinary ; Physics, Applied |
| Funding Project | 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 |
| Funding Organization | 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 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://dspace.imech.ac.cn/handle/311007/95430 |
| Collection | 非线性力学国家重点实验室 |
| Corresponding Author | Meng, Fanqiang; Tang, Chuanbao; Lu, Chenyang |
| Affiliation | 1.Xi An Jiao Tong Univ, Sch Nucl Sci & Technol, Xian 710049, Peoples R China 2.Sun Yat sen Univ, Sino French Inst Nucl Engn & Technol, Zhuhai 519082, Peoples R China 3.Nucl Power Inst China, Chengdu 610213, Peoples R China 4.Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100190, Peoples R China 5.Univ Chinese Acad Sci, Ctr Mat Sci & Optoelect Engn, Beijing 100049, Peoples R China 6.Guangdong Aerosp Res Acad, Guangzhou 511458, Peoples R China 7.Xi An Jiao Tong Univ, State Key Lab Multiphase Flow Power Engn, Xian 710049, Peoples R China |
| Recommended Citation 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. |
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