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作 者:Ying Zhang Ke Ren William Yi Wang Xingyu Gao Jun Wang Yiguang Wang Haifeng Song Xiubing Liang Jinshan Li
机构地区:[1]State Key Laboratory of Solidification Processing,Northwestern Polytechnical University,Xi’an,Shaanxi,China [2]Innovation Center,NPU Chongqing,Chongqing,China [3]Institute of Advanced Structure Technology,Beijing Institute of Technology,Beijing,China [4]Laboratory of Computational Physics,Institute of Applied Physics and Computational Mathematics,Beijing,China [5]Defense Innovation Institute,Academy of Military Sciences of the PLA of China,Beijing,China
出 处:《npj Computational Materials》2024年第1期249-258,共10页计算材料学(英文)
基 金:supported by National Defense Basic Scientific Research(Grant No.211-CXCY-N103-03-04-00 and 2022-JCKY-JJ-1086).
摘 要:The fracture toughness(KIC)of high-entropy oxides(HEOs)is critically important for several applications,but identification and quantification of the toughening mechanisms resulting from latticeengineering/distortion in HEOs is challenging.Here,based on the classic Griffith criteria,a physicsdriven theoretical equation combined with a knowledge-enabled data-driven machine-learning algorithm is proposed to predict the KIC and elucidate the toughening mechanisms of A_(2)Zr_(2)O_(7)-type HEOs.Together with experimental verification,our proposed model is applied to a dataset comprising 41208(nRE1/n)_(2)Zr_(2)O_(7)(n=2~7)HEOs,considering the contributions of the intrinsic brittleness and increased toughness due to the local lattice distortion(LLD),thereby addressing the challenge of accurate estimating KIC in complex HEOs using the rule of mixtures.During crack tip propagation,the interaction mechanism of cations induces stress fields and charge variations of LLD and dissipates crack energy,thus,to yield the crack tip softening and the elastic shielding and to enhance the toughness of HEOs.
分 类 号:TG1[金属学及工艺—金属学]
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