Search for: All records

We perform a systematic investigation of several crystal structures, based on monolayer MnBi2Te4, of the form MnBiBiiXi2Xii2 using first-principles calculations. Our analysis shows that the most energetically favorable bonding configuration of the constituent elements in monolayer MnBiBiiXi2Xii2 is determined by the bond length between the Mn atom and its nearest X-site atoms. Tuning the bonding configuration of the material alters the magnetic, electronic, and topological properties. We also calculate the magnetic exchange parameters and magnetic anisotropy energy of the predicted structures. The calculations show that the elements at the X sites mainly determine the magnetic properties. Finally, we propose a stable phase of monolayer MnBi2S2Te2 (i.e., γ-MnBi2S2Te2) that exhibits the quantum anomalous Hall effect (QAHE). This study demonstrates that the bonding configuration of MnBi2Te4-type materials provides avenues for tuning the magnetic, electronic, and topological properties of van der Waals (vdW) materials. 

Strain-induced topological phase transition in the ferromagnetic Janus monolayer MnSbBiS₂Te₂is displayed. 

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 Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many fields. Materials science, in particular, encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC), and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website:https://pages.nist.gov/jarvis_leaderboard/