晶格常数
电负性
离子半径
格子(音乐)
晶体结构
材料科学
常量(计算机编程)
立方晶系
凝聚态物理
机器学习
统计物理学
计算机科学
离子
物理
结晶学
化学
光学
衍射
量子力学
程序设计语言
声学
作者
Inas Bouzateur,Hamza Bennacer,Ouali Mohamed Assam,Mohamed Ladjal,Ziane Mohamed Issam,Moufdi Hadjab
标识
DOI:10.1109/icateee57445.2022.10093696
摘要
Double perovskite oxides have received a lot of interest in the last ten years because of their distinctive and adaptable material properties. Among the six parameters in the cubic structure, the lattice constant is the sole changeable parameter, which plays an important role in developing materials for particular technological applications and distinctively identifies the crystal structure of the material. In this paper, the extreme learning machine (ELM) is used to correlate the lattice constant of $A_2^{ + 2}BC{O_6}$ cubic perovskite compounds, such as their ionic radii, electronegativity, oxidation state, and lattice constant. We investigated 147 compounds with lattice constants between 7.700 and 8.890Å. The prediction method has a high level of accuracy and stability and provides accurate estimates of lattice constants.
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