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Updated: Sep 11, 2025

Quantitative Atomic-Site Analysis of Functional Dopants/Point Defects in Crystalline Materials by Electron-Channeling-Enhanced Microanalysis
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关于无形HfO中氧空缺缺陷的统计:一个神经网络原子间潜力辅助高通量预测

Shuqi Tang1, Kang Wang1, Menglin Huang1

  • 1College of Integrated Circuits and Micro-Nano Electronics, and Key Laboratory of Computational Physical Sciences (MOE), Fudan University, Shanghai, 200433, China.

Small methods
|August 18, 2025
PubMed
概括

预测无形材料中的缺陷特性是一项挑战. 本研究引入了一个图形神经网络潜力,用于准确计算无形氧氧氧氧化物中缺陷的缺陷,建立可靠超级细胞建模的标准.

关键词:
在HfO2中,二氧化碳是最重要的.无形材料是无形的材料.缺陷统计数据 缺陷统计数据神经网络 原子间潜力 神经网络 原子间潜力氧气空缺的地方.

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