实现智能扫描传输电子显微镜,使用高性能计算
Utkarsh Pratiush1, Austin Houston1, Sergei V Kalinin1,2
1Department of Materials Science and Engineering, University of Tennessee, Knoxville, Tennessee 37996, USA.
The Review of scientific instruments
|October 1, 2024
概括
这项研究将扫描传输电子显微镜 (STEM) 与电子能量损失光谱 (EELS) 以及在高性能计算 (HPC) 系统上的机器学习 (ML) 整合在一起. 这提高了材料表征的效率和全球STEM用户的范围.
科学领域:
- 材料科学 材料科学 材料科学
- 分析化学 分析化学
- 计算科学 计算科学
背景情况:
- 扫描传输电子显微镜 (STEM) 与电子能量损失光谱 (EELS) 结合,为材料表征提供了丰富的数据.
- 现代电子显微镜以超过人类感知速度生成数据.
- 机器学习 (ML) 提供了通过积极学习增强STEM-EELS能力的潜力.
研究的目的:
- 将ML算法集成到STEM-EELS框架中,以进行增强的材料表征.
- 为了实现STEM与高性能计算 (HPC) 系统的无集成.
- 开发和演示用于先进材料分析的工作流程.
主要方法:
- 开发了Python服务器软件,作为DigitalMicrograph硬件模块的包装.
- 促进了STEM-EELS数据采集和分析的远程计算机交互.
- 实现了复杂的工作流程,包括对象查找和深度内核学习.
主要成果:
- 证明了STEM-EELS与ML和HPC系统的无集成.
- 展示了提高效率和扩大材料表征的范围.
- 通过Gatan图像过器为全球STEM用户启用了先进的分析技术.
结论:
- STEM-EELS,ML和HPC的融合显著提升了材料表征能力.
- 开发的软件和工作流程适用于广泛的STEM仪器.
- 在GitHub上开源代码的可用性促进了更广泛的采用和进一步开发.
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