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

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 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.

Updates from final journal publication

References in corpus (4)

• Evolution of Interlayer Coupling in Twisted MoS2 Bilayers
• Room-temperature optically detected magnetic resonance of single defects in hexagonal boron nitride
• How Substitutional Point Defects in Two-Dimensional WS${}_2$ Induce Charge Localization, Spin-Orbit Splitting, and Strain
• Revealing the chemical bonding in adatoms arrays via machine learning of 3D scanning tunneling spectroscopy data

Cited by in corpus (7)

• A substitutional quantum defect in WS${}_2$ discovered by high-throughput computational screening and fabricated by site-selective STM manipulation
• Machine learning aided atomic structure identification of interfacial ionic hydrates from atomic force microscopy images
• Post-Experiment Forensics and Human-in-the-Loop Interventions in Explainable Autonomous Scanning Probe Microscopy
• Advanced Techniques in Automated High Resolution Scanning Transmission Electron Microscopy
• Unraveling the Impact of Initial Choices and In-Loop Interventions on Learning Dynamics in Autonomous Scanning Probe Microscopy
• Graphene-driven correlated electronic states in one dimensional defects within WS${}_2$
• Compactly-supported nonstationary kernels for computing exact Gaussian processes on big data