Development of machine learning and empirical interatomic potentials for the binary Zr-Sn system-Reference-Cited by-同舟云学术

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Development of machine learning and empirical interatomic potentials for the binary Zr-Sn system

Published:2024-01 Issue: Volume:588 Page:154794
ISSN:0022-3115
Container-title:Journal of Nuclear Materials
language:en
Short-container-title:Journal of Nuclear Materials

Author:

Mei Haojie,Chen Liang,Wang Feifei,Liu Guisen,Hu Jing,Lin Weitong,Shen Yao,Li Jinfu,Kong Lingti

Publisher

Elsevier BV

Subject

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

Reference140 articles.

1. Irradiation creep and growth of zirconium alloys: A critical review

2. Corrosion of Zirconium Alloys Used for Nuclear Fuel Cladding

3. Effects of ion irradiation on microstructure and properties of zirconium alloys—A review

4. Two-dimensional vacancy platelets as precursors for basal dislocation loops in hexagonal zirconium

5. Fundamentals of Radiation Materials Science : Metals and Alloys;Was,2017

Cited by 6 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Prediction of the swelling rate of irradiated type 316 stainless steels via machine learning methods;Journal of Nuclear Materials;2024-11

2. Atomic-scale investigation of interactions between Cu solute and defects in Zr alloys;Journal of Nuclear Materials;2024-08

3. Improvement and prediction technology of the water-side corrosion of zirconium alloy: the developmental tendency;Journal of Physics: Conference Series;2024-08-01

4. A Comparative Study on Force-Fields for Interstitial Diffusion in α-Zr and Zr Alloys;Materials;2024-07-23

5. A Genetic Algorithm Trained Machine-Learned Interatomic Potential for the Silicon–Carbon System;The Journal of Physical Chemistry C;2024-07-16

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