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Scandium nitride (ScN) has recently attracted much attention for its potential applications in thermoelectric energy conversion, as a semiconductor in epitaxial metal/semiconductor superlattices, as a substrate for GaN growth, and alloying it with AlN for 5G technology. This study was undertaken to better understand its stoichiometry and electronic structure. ScN (100) single crystals 2 mm thick were grown on a single crystal tungsten (100) substrate by a physical vapor transport method over a temperature range of 1900–2000 °C and a pressure of 20 Torr. The core level spectra of Sc 2p3/2,1/2 and N 1s were obtained by x-ray photoelectron spectroscopy (XPS). The XPS core levels were shifted by 1.1 eV toward higher values as the [Sc]:[N] ratio varied from 1.4 at 1900 °C to ∼1.0 at 2000 °C due to the higher binding energies in stoichiometric ScN. Angle-resolved photoemission spectroscopy measurements confirmed that ScN has an indirect bandgap of ∼1.2 eV.

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 WS₂sulfur 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.