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Abstract The family of transition-metal dipnictides has been of theoretical and experimental interest because this family hosts topological states and extremely large magnetoresistance (MR). Recently, ${\mathrm{T}\mathrm{a}\mathrm{A}\mathrm{s}}_2$, a member of this family, has been predicted to support a topological crystalline insulating state. Here, by using high-resolution angle-resolved photoemission spectroscopy (ARPES), we reveal both closed and open pockets in the metallic Fermi surface (FS) and linearly dispersive bands on the ( $\bar{2}01$) surface, along with the presence of extreme MR observed from magneto-transport measurements. A comparison of the ARPES results with first-principles computations shows that the linearly dispersive bands on the measured surface of ${\mathrm{T}\mathrm{a}\mathrm{A}\mathrm{s}}_2$are trivial bulk bands. The absence of symmetry-protected surface state on the ( $\bar{2}01$) surface indicates its topologically dark nature. The presence of open FS features suggests that the open-orbit fermiology could contribute to the extremely large MR of ${\mathrm{T}\mathrm{a}\mathrm{A}\mathrm{s}}_2$. 

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 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.