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arXiv 2110.03351cond-mat.mtrl-sciphysics.app-ph
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WS $_2$和Au{111}的扫描探针显微镜自主研究

Autonomous Investigations over WS$_2$ and Au{111} with Scanning Probe Microscopy

John C. Thomas, Antonio Rossi, Darian Smalley, Luca Francaviglia, Zhuohang Yu, Tianyi Zhang, Shalini Kumari, Joshua A. Robinson, Mauricio Terrones, Masahiro Ish… 展开作者

John C. Thomas, Antonio Rossi, Darian Smalley, Luca Francaviglia, Zhuohang Yu, Tianyi Zhang, Shalini Kumari, Joshua A. Robinson, Mauricio Terrones, Masahiro Ishigami, Eli Rotenberg, Edward S. Barnard, Archana Raja, Ed Wong, D. Frank Ogletree, Marcus M. Noack, Alexander Weber-Bargioni

摘要:

二维材料中的单个原子缺陷会影响其宏观功能。关联的相互作用是具有挑战性的,然而,智能高光谱扫描隧道光谱(STS)映射提供了一个可行的解决方案,这个技术上困难和耗时的问题。在这里,通过高斯过程回归自主收集密集的光谱体积,其中卷积神经网络串联用于光谱识别。所采集的数据能够进行缺陷分割,并提供了一个工作流程,用于在实验期间进行机器驱动的决策,并具有用户定制的能力。我们提供了一种实现自主实验的方法,以增强再现性和用户可访问性。探索了WS $_2$硫空位的高光谱研究,并结合Au{111}人字形重构的局域态密度确认.硫族元素空位,原始WS $_2$,金面心立方,金六方密堆积区域进行检查和检测的机器学习方法,以证明人工智能的高光谱STS映射的潜力。

英文摘要:

Individual atomic defects in 2D materials impact their macroscopic functionality. Correlating the interplay is challenging, however, intelligent hyperspectral scanning tunneling spectroscopy (STS) mapping provides a feasible solution to this technically difficult and time consuming problem. Here, dense spectroscopic volume is collected autonomously via Gaussian process regression, where convolutional neural networks are used in tandem for spectral identification. Acquired data enable defect segmentation, and a workflow is provided for machine-driven decision making during experimentation with capability for user customization. We provide a means towards autonomous experimentation for the benefit of both enhanced reproducibility and user-accessibility. Hyperspectral investigations on WS$_2$ sulfur vacancy sites are explored, which is combined with local density of states confirmation on the Au{111} herringbone reconstruction. Chalcogen vacancies, pristine WS$_2$, Au face-centered cubic, and Au hexagonal close packed regions are examined and detected by machine learning methods to demonstrate the potential of artificial intelligence for hyperspectral STS mapping.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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