Autonomous Scanning Probe Microscopy Investigations over WS2 and Au{111}
Creators
- 1. Molecular Foundry, Lawrence Berkeley National Laboratory, CA 94720, United States of America
- 2. Advanced Light Source, Lawrence Berkeley National Laboratory, CA 94720, United States of America
- 3. Department of Physics, University of Central Florida, Orlando, FL 32816, United States of America
- 4. Department of Materials Science and Engineering, The Pennsylvania State University, University Park, PA 16082 United States of America
- 5. Center for Advanced Mathematics for Energy Research Applications, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, United States of America
Description
Dataset for associated publication.
Abstract:
Point defect identification in two-dimensional materials enables an understanding of the local environment within a given system, where scanning probe microscopy that takes advantage of hyperspectral tunneling bias spectroscopy acquisition can both map and identify the atomic and electronic landscape. Here, dense spectroscopic volume is collected autonomously via Gaussian process regression, where convolutional neural networks are used in tandem for defect identification. Acquired data enable image segmentation across defect modes, and a workflow is provided for both machine-driven decision making during experimentation and the capability for user customization. Where scanning tunneling microscopy and spectroscopy can be nontrivial and time intensive, we provide a means towards autonomous experimentation for the benefit of both enhanced reproducibility and user-accessibility. Monolayer semiconductor is explored on tungsten disulfide sulfur vacancies to provide two-dimensional hyperspectral insight into available sulfur-substitution sites within a transition metal dichalcogenide (TMD), which is combined with spectral confirmation on the Au{111} herringbone reconstruction for both tip state verification and local fingerprinting. Overall, chalcogen vacancies, pristine TMD, Au face-centered cubic, and Au hexagonal close packed regions are examined and detected by machine learning methods.
Files
gpSTS_spectroscopy.zip
Files
(24.5 MB)
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Additional details
Related works
- Is cited by
- Journal article: 10.1038/s41524-022-00777-9 (DOI)
- Is published in
- Preprint: arXiv:2110.03351 (arXiv)