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AbstractCourse-based Undergraduate Research Experiences (CUREs) bring the excitement of research into the classroom to improve learning and the sense of belonging in the field. They can reach more students, earlier in their studies, than typical undergraduate research. Key aspects are: students learn and use research methods, give input into the project, generate new research data, and analyze it to draw conclusions that are not known beforehand. CUREs are common in other fields but have been rare in materials science and engineering. I propose a paradigm for computational material science CUREs, enabled by web-based simulation tools from nanoHUB.org that require minimal computational skills. After preparatory exercises, students each calculate part of a set of closely related materials, following a defined protocol to contribute to a novel class dataset which they analyze, and also calculate an additional property of their choice. This approach has been used successfully in several class projects. Graphical abstract 

Abstract Point defects in two-dimensional materials are of key interest for quantum information science. However, the parameter space of possible defects is immense, making the identification of high-performance quantum defects very challenging. Here, we perform high-throughput (HT) first-principles computational screening to search for promising quantum defects within WS₂, which present localized levels in the band gap that can lead to bright optical transitions in the visible or telecom regime. Our computed database spans more than 700 charged defects formed through substitution on the tungsten or sulfur site. We found that sulfur substitutions enable the most promising quantum defects. We computationally identify the neutral cobalt substitution to sulfur (Co$${}_{{{{{{{{\rm{S}}}}}}}}}^{0}$$ ${}_S^0$) and fabricate it with scanning tunneling microscopy (STM). The Co$${}_{{{{{{{{\rm{S}}}}}}}}}^{0}$$ ${}_S^0$electronic structure measured by STM agrees with first principles and showcases an attractive quantum defect. Our work shows how HT computational screening and nanoscale synthesis routes can be combined to design promising quantum defects. 

Abstract Spin-flip (SF) methods applied to excited-state approaches like the Bethe–Salpeter equation allow access to the excitation energies of open-shell systems, such as molecules and defects in solids. The eigenstates of these solutions, however, are generally not eigenstates of the spin operator ${\hat{S}}^2$. Even for simple cases where the excitation vector is expected to be, for example, a triplet state, the value of $⟨{\hat{S}}^2⟩$may be found to differ from 2.00; this difference is called ‘spin contamination’. The expectation values $⟨{\hat{S}}^2⟩$must be computed for each excitation vector, to assist with the characterization of the particular excitation and to determine the amount of spin contamination of the state. Our aim is to provide for the first time in the SF methods literature a comprehensive resource on the derivation of the formulas for $⟨{\hat{S}}^2⟩$as well as its computational implementation. After a brief discussion of the theory of the SF Bethe–Salpeter equation (BSE) and some examples further illustrating the need for calculating $⟨{\hat{S}}^2⟩$, we present the derivation for the general equation for computing $⟨{\hat{S}}^2⟩$with the eigenvectors from an SF-BSE calculation, how it is implemented in a Python script, and timing information on how this calculation scales with the size of the SF-BSE Hamiltonian.