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深度卷积神经网络的性能,用于使用包含格子缺陷信息的选定区域电子束衍射模式来分类晶体结构

Jae Min Jeong¹, Moonsoo Ra², Jinha Jeong²

¹Dept. of Materials Convergence and System Engineering, Changwon National University 20 Changwondaehak-ro Changwon-si Gyeongsangnam-do 51140 Republic of Korea woonglee@changwon.ac.kr.

|June 11, 2024

概括

一个深层卷积神经网络 (ResNet) 显示了分析晶体缺陷的电子衍射模式的潜力. 虽然它对格子振动和应变非常准确,但它与位移和双边界扎,需要进一步的训练.

科学领域:

材料科学材料科学材料科学
计算材料科学科学计算材料科学
机器学习应用机器学习应用

背景情况:

选择区域电子衍射模式 (SADP) 包含关于晶格缺陷的关键信息.
深度卷积神经网络 (DCNNs) 在分析复杂材料数据方面表现有前途.
准确的缺陷识别对于理解材料特性至关重要.

研究的目的:

评估ResNet架构在对具有各种格子缺陷的SADP数据集进行分类时的性能.
探索数据集增强策略,以提高DCNN在材料科学中的性能.
评估ResNet识别应变,热振动,点缺陷,位移和双边界的能力.

主要方法:

模拟的SADP数据集包含格子缺陷 (菌株,振动,点缺陷,位移,双边界).
利用*ab initio*分子动力学,第一原理几何优化,以及用于缺陷模拟的晶格操纵.
在模拟的SADP数据集上训练并测试了预先训练的ResNet模型.

主要成果:

对于格子振动和点缺陷,ResNet实现了可接受的分类准确性,随着障碍的增加,性能下降.
该模型在高应变水平下显示了对格子对称性变化的敏感性.
ResNet无法准确地识别受脱位和双边界影响的晶体结构.

结论:

ResNet显示了SADP中缺陷分析的潜力,但需要进一步开发以全面识别缺陷.
未来的DCNN架构应接受各种缺陷场景的培训,以提高一般分类性能.
加强DCNN以识别与缺陷相关的衍射模式中的微妙特征变化至关重要.

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