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

Abstract

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.

Generalised deep-learning workflow for the prediction of hydration layers over surfaces

“…Our approach links strongly to the developing DL methods applied to data challenges in Scanning Probe Microscopy (SPM) [30][31][32][33]. In particular, the success of deep learning Convolutional Neural Networks (CNN) [34] in image recognition tasks has led to their application to the analysis of SPM images [35], especially in the context of molecular/surface feature/defect characterisation [36][37][38][39][40][41], scanning-probe characterisation [42,43], and techniques for autonomously-driven SPM [44][45][46].…”

WS2 Band Gap Renormalization Induced by Tomonaga Luttinger Liquid Formation in Mirror Twin Boundaries

“…In order to better investigate the physics of the MTB bandgap formed in WS 2 , we make use of both point STS and differential conductance mapping, which are powerful tools for screening defects. 28,37,47 Electronic band-edge state differential conductance maps, acquired by dI/dV imaging (Fig. 2 (a-f)), at the LUS (ψ − ) and the HOS (ψ + ) are spatially out-of-phase for the SMTB case, and are a spatially in-phase for an AMGB.…”

WS$_2$ Band Gap Renormalization Induced by Tomonaga Luttinger Liquid Formation in Mirror Twin Boundaries

“…In order to better investigate the physics of the MTB bandgap formed in WS 2 , we make use of both point STS and differential conductance mapping, which are powerful tools for screening defects. 28,37,47 Electronic band-edge state differential conductance maps, acquired by dI/dV imaging (Fig. 2 (a-f)), at the LUS (ψ − ) and the HOS (ψ + ) are spatially out-of-phase for the SMTB case, and are a spatially in-phase for an AMGB.…”