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In this work, we investigate magnetic monolayers of the form A i A ii B 4 X 8 based on the well-known intrinsic topological magnetic van der Waals (vdW) material MnBi 2 Te 4 (MBT) using first-principles calculations and machine learning techniques. We select an initial subset of structures to calculate the thermodynamic properties, electronic properties, such as the band gap, and magnetic properties, such as the magnetic moment and magnetic order using density functional theory (DFT). Data analytics approaches are used to gain insight into the microscopic origin of materials’ properties. The dependence of materials’ properties on chemical composition is also explored. For example, we find that the formation energy and magnetic moment depend largely on A and B sites whereas the band gap depends on all three sites. Finally, we employ machine learning tools to accelerate the search for novel vdW magnets in the MBT family with optimized properties. This study creates avenues for rapidly predicting novel materials with desirable properties that could enable applications in spintronics, optoelectronics, and quantum computing. 

Abstract We report a combined experimental and computational study of the optical properties of individual silicon telluride (Si 2 Te 3 ) nanoplates. The p-type semiconductor Si 2 Te 3 has a unique layered crystal structure with hexagonal closed-packed Te sublattices and Si–Si dimers occupying octahedral intercalation sites. The orientation of the silicon dimers leads to unique optical and electronic properties. Two-dimensional Si 2 Te 3 nanoplates with thicknesses of hundreds of nanometers and lateral sizes of tens of micrometers are synthesized by a chemical vapor deposition technique. At temperatures below 150 K, the Si 2 Te 3 nanoplates exhibit a direct band structure with a band gap energy of 2.394 eV at 7 K and an estimated free exciton binding energy of 150 meV. Polarized reflection measurements at different temperatures show anisotropy in the absorption coefficient due to an anisotropic orientation of the silicon dimers, which is in excellent agreement with theoretical calculations of the dielectric functions. Polarized Raman measurements of single Si 2 Te 3 nanoplates at different temperatures reveal various vibrational modes, which agree with density functional perturbation theory calculations. The unique structural and optical properties of nanostructured Si 2 Te 3 hold great potential applications in optoelectronics and chemical sensing. 

Layered IV−VI2 compounds often exist in a CdI2 structure. Using the evolution algorithm and first-principles calculations, we predict a novel layered structure of silicon ditelluride (SiTe2) that is more stable than the CdI2 phase. The structure has a triclinic unit cell in its bulk form. The atomic arrangement indicates the competition between the Si atoms’ tendency to form tetrahedral bonds and the Te atoms’ tendency to form hexagonal close-packing. The electronic and vibrational properties of the predicted phase are investigated. The effective mass of an electron is small among two-dimensional (2D) semiconductors, which is beneficial for applications such as field-effect transistors. The vibrational Raman and IR spectra are calculated to facilitate future experimental investigations. 

ABSTRACT Silicon telluride (Si2Te3) is a silicon-based 2D chalcogenide with potential applications in optoelectronics. It has a unique crystal structure where Si atoms form Si-Si dimers to occupy the “metal” sites. In this paper, we report an ab initio computational study of its optical dielectric properties using the GW approximation and the Bethe-Salpeter equation (BSE). Strong in-plane optical anisotropy is discovered. The imaginary part of the dielectric constant in the direction parallel to the Si-Si dimers is found to be much lower than that perpendicular to the dimers. The optical measurement of the absorption spectra of 2D Si2Te3 nanoplates shows modulation of the absorption coefficient under 90-degree rotation, confirming the computational results. We show the optical anisotropy originates from the particular compositions of the wavefunctions in the valence and conduction bands. Because it is associated with the Si dimer orientation, the in-plane optical anisotropy can potentially be dynamically controlled by electrical field and strain, which may be useful for new device design. In addition, BSE calculations reduce GW quasiparticle band gap by 0.3 eV in bulk and 0.6 eV in monolayer, indicating a large excitonic effect in Si2Te3. Furthermore, including electron-hole interaction in bulk calculations significantly reduces the imaginary part of the dielectric constant in the out-of-plane direction, suggesting strong interlayer exciton effect in Si2Te3 multilayers. 

A materials informatics framework to explore a large number of candidate van der Waals (vdW) materials is developed. In particular, in this study a large space of monolayer transition metal halides is investigated by combining high‐throughput density functional theory calculations and artificial intelligence (AI) to accelerate the discovery of stable materials and the prediction of their magnetic properties. The formation energy is used as a proxy for chemical stability. Semi‐supervised learning is harnessed to mitigate the challenges of sparsely labeled materials data in order to improve the performance of AI models. This approach creates avenues for the rapid discovery of chemically stable vdW magnets by leveraging the ability of AI to recognize patterns in data, to learn mathematical representations of materials from data and to predict materials properties. Using this approach, previously unexplored vdW magnetic materials with potential applications in data storage and spintronics are identified.