Atomistic insights into early stage corrosion of bcc Fe surfaces in oxygen dissolved liquid lead-bismuth eutectic(LBE-O)  

作　　者：周婷 高星 马志伟 常海龙 申铁龙 崔明焕 王志光 Ting Zhou;Xing Gao;Zhiwei Ma;Hailong Chang;Tielong Shen;Minghuan Cui;Zhiguang Wang(Institute of Modern Physics,Chinese Academy of Sciences,Lanzhou 730000,China;School of Nuclear Science and Technology,University of Chinese Academy of Sciences,Beijing 100049,China)

机构地区：[1]Institute of Modern Physics,Chinese Academy of Sciences,Lanzhou 730000,China [2]School of Nuclear Science and Technology,University of Chinese Academy of Sciences,Beijing 100049,China

出　　处：《Chinese Physics B》2023年第3期384-395,共12页中国物理B（英文版）

基　　金：the National Natural Science Foundation of China(Grant No.U1832206).

摘　　要：Classical molecular dynamics simulations with global neural network machine learning potential are used to study early stage oxidation and dissolution behaviors of bcc Fe surfaces contacting with stagnant oxygen dissolved liquid leadbismuth eutectic(LBE-O).Both static and dynamic simulation results indicate that the early stage oxidation and dissolution behaviors of bcc Fe show strong orientation dependence under the liquid LBE environments,which may explain the experimental observations of uneven interface between iron-based materials and liquid LBE.Our investigations show that it is the delicate balance between the oxide growth and metal dissolution that leads to the observed corrosion anisotropy for bcc Fe contacting with liquid LBE-O.

关 键 词：liquid lead-bismuth eutectic(LBE) global neural network(G-NN)potential DISSOLUTION 

分 类 号：TG172[金属学及工艺—金属表面处理]