Autonomous scanning probe microscopy investigations over WS2 and Au{111}

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

doi:10.1038/s41524-022-00777-9

Title: Autonomous scanning probe microscopy investigations over WS₂ and Au{111}

Journal Article · 02 May 2022 · npj Computational Materials

DOI: https://doi.org/10.1038/s41524-022-00777-9 · OSTI ID:1865998

^[1]; Rossi, Antonio ^[1]; Smalley, Darian ^[2];

^[1]; Yu, Zhuohang ^[3]; Zhang, Tianyi ^[3]; Kumari, Shalini ^[3]; Robinson, Joshua A. ^[3]; Terrones, Mauricio ^[3]; Ishigami, Masahiro ^[2]; Rotenberg, Eli ^[1];

^[1]; Raja, Archana ^[1]; Wong, Ed ^[1];

^[1];

^[1]; Weber-Bargioni, Alexander ^[1]

Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Univ. of Central Florida, Orlando, FL (United States)
Pennsylvania State Univ., University Park, PA (United States)

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 WS₂ sulfur vacancy sites are explored, which is combined with local density of states confirmation on the Au{111} herringbone reconstruction. Chalcogen vacancies, pristine WS₂, 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.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); USDOE Office of Science (SC), Basic Energy Sciences (BES); National Science Foundation (NSF); Swiss National Science Foundation (SNSF); USDOE

Grant/Contract Number:: AC02-05CH11231; 2002651; 2011839; P2ELP2_184398

OSTI ID:: 1865998

Alternate ID(s):: OSTI ID: 1880990

Journal Information:: npj Computational Materials, Vol. 8, Issue 1; ISSN 2057-3960

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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Autonomous Scanning Probe Microscopy Investigations over WS2 and Au{111} Thomas, John C.; Rossi, Antonio; Smalley, Darian Zenodo https://doi.org/10.5281/zenodo.5768320	dataset	January 2021

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