Strain-induced topological phase transition in ferromagnetic Janus monolayer MnSbBiS2Te2.

We investigate a strain-induced topological phase transition in the ferromagnetic Janus monolayer MnSbBiS2Te2 using first-principles calculations. The electronic, magnetic, and topological properties are studied under biaxial strain within the range of -8 to +8%. The ground state of monolayer MnSbBiS2Te2 is metallic with an out-of-plane magnetic easy axis. A band gap is opened when a compressive strain between -4% and -7% is applied. We observe a topological phase transition at a biaxial strain of -5%, where the material becomes a Chern insulator exhibiting a quantum anomalous hall (QAH) effect. We find that biaxial strain and spin-orbit coupling (SOC) are responsible for the topological phase transition in MnSbBiS2Te2. In addition, we find that biaxial strain can alter the direction of the magnetic easy axis of MnSbBiS2Te2. The Curie temperature is calculated using the Heisenberg model and is found to be 24 K. This study could pave the way to the design of topological materials with potential applications in spintronics, quantum computing, and dissipationless electronics.

Effect of Dual-Organic Cations on the Structure and Properties of 2D Hybrid Perovskites as Scintillators.

Two-dimensional (2D) hybrid organic-inorganic perovskite (HOIP) crystals show promise as scintillating materials for wide-energy radiation detection, outperforming their three-dimensional counterparts. In this study, we synthesized single crystals of (PEA2-xBZAx)PbBr4 (x ranging from 0.1 to 2), utilizing phenethylammonium (C6H5CH2CH2NH3+) and benzylammonium (C6H5CH2NH3+) cations. These materials exhibit favorable optical and scintillation properties, rendering them suitable for high light yield (LY) and fast-response scintillators. Our investigation, employing various techniques such as X-ray diffraction (XRD), photoluminescence (PL), time-resolved (TR) PL, Raman spectroscopy, radioluminescence (RL), thermoluminescence (TL), and scintillation measurements, unveiled lattice strain induced by dual-organic cations in powder X-ray diffraction. Density functional theory analysis demonstrated a maximal 0.13 eV increase in the band gap with the addition of BZA cation addition. Notably, the largest Stokes shift of 0.06 eV was observed in (BZA)2PbBr4. The dual-organic cation crystals displayed >80% fast component scintillation decay time, which is advantageous for the scintillating process. Furthermore, we observed a dual-organic cations-induced enhancement of electron-hole transfer efficiency by up to 60%, with a contribution of >70% to the fast component of scintillation decay. The crystal with the lowest BZA concentration, (PEA1.9BZA0.1)PbBr4, demonstrated the highest LYs of 14.9 ± 1.5 ph/keV at room temperature. Despite a 55-70% decrease in LY for BZA concentrations >5%, simultaneous reductions in scintillation decay time (12-32%) may work for time-of-flight positron emission tomography and photon-counting computed tomography. Our work underscores the crucial role of dual-organic cations in advancing our understanding of 2D-HOIP crystals for materials science and radiation detection applications.

Machine Learning-Aided Band Gap Engineering of BaZrS3 Chalcogenide Perovskite.

The non-toxic and stable chalcogenide perovskite BaZrS3 fulfills many key optoelectronic properties for a high-efficiency photovoltaic material. It has been shown to possess a direct band gap with a large absorption coefficient and good carrier mobility values. With a reported band gap of 1.7-1.8 eV, BaZrS3 is a good candidate for tandem solar cell materials; however, its band gap is significantly larger than the optimal value for a high-efficiency single-junction solar cell (∼1.3 eV, Shockley-Queisser limit)─thus doping is required to lower the band gap. By combining first-principles calculations and machine learning algorithms, we are able to identify and predict the best dopants for the BaZrS3 perovskites for potential future photovoltaic devices with a band gap within the Shockley-Queisser limit. It is found that the Ca dopant at the Ba site or Ti dopant at the Zr site is the best candidate dopant. Based on this information, we report for the first time partial doping at the Ba site in BaZrS3 with Ca (i.e., Ba1-xCaxZrS3) and compare its photoluminescence with Ti-doped perovskites [i.e., Ba(Zr1-xTix)S3]. Synthesized (Ba,Ca)ZrS3 perovskites show a reduction in the band gap from ∼1.75 to ∼1.26 eV with <2 atom % Ca doping. Our results indicate that for the purpose of band gap tuning for photovoltaic applications, Ca-doping at the Ba-site is superior to Ti-doping at the Zr-site reported previously.

Data-Driven Studies of the Magnetic Anisotropy of Two-Dimensional Magnetic Materials.

A key issue in layered materials is the dependence of their properties on their chemical composition and crystal structure in addition to the dimensionality. For instance, atomically thin magnetic structures exhibit novel spin properties that do not exist in the bulk. We use first-principles calculations, based on density functional theory, and machine learning to study the magnetocrystalline anisotropy of a set of single-layer two-dimensional structures that are derived from changing the chemical composition of the ferromagnetic semiconductor Cr2Ge2Te6. We discuss trends and identify descriptors for the magnetocrystalline anisotropy in monolayers with the chemical formula A2B2X6. Our data-driven study aims to provide physical insights into the microscopic origins of magnetic anisotropy in two dimensions. For instance, we demonstrate that hybridization plays a key role in determining the magnetic anisotropy of the materials investigated in this study. In addition, we demonstrate that first-principles calculations can be combined with machine learning to create a high-throughput computational approach for the targeted design of quantum materials with potential applications in areas ranging from sensing to data storage.

Data-driven studies of magnetic two-dimensional materials.

We use a data-driven approach to study the magnetic and thermodynamic properties of van der Waals (vdW) layered materials. We investigate monolayers of the form [Formula: see text], based on the known material [Formula: see text], using density functional theory (DFT) calculations and machine learning methods to determine their magnetic properties, such as magnetic order and magnetic moment. We also examine formation energies and use them as a proxy for chemical stability. We show that machine learning tools, combined with DFT calculations, can provide a computationally efficient means to predict properties of such two-dimensional (2D) magnetic materials. Our data analytics approach provides insights into the microscopic origins of magnetic ordering in these systems. For instance, we find that the X site strongly affects the magnetic coupling between neighboring A sites, which drives the magnetic ordering. Our approach opens new ways for rapid discovery of chemically stable vdW materials that exhibit magnetic behavior.