Prediction of vacancy formation energies at tungsten grain boundaries from local structure via machine learning method-Reference-Cited by-同舟云学术

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Prediction of vacancy formation energies at tungsten grain boundaries from local structure via machine learning method

Published:2022-02 Issue: Volume:559 Page:153412
ISSN:0022-3115
Container-title:Journal of Nuclear Materials
language:en
Short-container-title:Journal of Nuclear Materials

Author:

Wang Yuxuan,Li Xiaolin,Li Xiangyan,Zhang Yuxiang,Zhang Yange,Xu Yichun,Lei Yawei,Liu C.S.,Wu Xuebang

Funder

National Natural Science Foundation of China

Natural Science Foundation of Anhui Province

National Key Research and Development Program of China

Publisher

Elsevier BV

Subject

Nuclear Energy and Engineering,General Materials Science,Nuclear and High Energy Physics

Reference61 articles.

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2. Structural materials for fission & fusion energy;Zinkle;Mater. Today,2009

3. Materials for the plasma-facing components of fusion reactors;Bolt;J. Nucl. Mater.,2004

4. Accumulation and recovery of defects in ion-irradiated nanocrystalline gold;Chimi;J. Nucl. Mater.,2001

5. Instability of irradiation induced defects in nanostructured materials;Rose;Nucl. Instrum. Methods Phys. Res. Sect. B,1997

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