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Register a new userMagnetic borophenes from evolutionary search
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A computation methodology based on ab initio evolutionary algorithms and the spin-polarized density functional theory was developed to predict two-dimensional (2D) magnetic materials. Its application to a model system borophene reveals an unexpected rich magnetism and polymorphism. A stable borophene with nonzero thickness was an antiferromagnetic (AFM) semiconductor from first-principles calculations, which can be further turned into a half metal by finite electron doping. In this borophene, the buckling and coupling among three atomic layers are not only responsible for the magnetism, but also result in an out-of-plane negative Poissons ratios under uniaxial tension, making it the first elemental material possessing auxetic and magnetic properties simultaneously.
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We study the mechanical properties of two-dimensional (2D) boron, borophenes, by first-principles calculations. The recently synthesized borophene with 1/6 concentration of hollow hexagons (HH) is shown to have in-plane modulus C up to 210 N/m and bending stiffness as low as D = 0.39 eV. Thus, its Foppl-von Karman number per unit area, defined as C/D, reaches 568 nm-2, over twofold higher than graphenes value, establishing the borophene as one of the most flexible materials. Yet, the borophene has a specific modulus of 346 m2/s2 and ideal strengths of 16 N/m, rivaling those (453 m2/s2 and 34 N/m) of graphene. In particular, its structural fluxionality enabled by delocalized multi-center chemical bonding favors structural phase transitions under tension, which result in exceptionally small breaking strains yet highly ductile breaking behavior. These mechanical properties can be further tailored by varying the HH concentration, and the boron sheet without HHs can even be stiffer than graphene against tension. The record high flexibility combined with excellent elasticity in boron sheets can be utilized for designing composites and flexible systems.
We show, by solving Maxwells equations, that an electric charge on the surface of a slab of a linear magnetoelectric material generates an image magnetic monopole below the surface provided that the magnetoelectric has a diagonal component in its magnetoelectric response. The image monopole, in turn, generates an ideal monopolar magnetic field outside of the slab. Using realistic values of the electric- and magnetic- field susceptibilties, we calculate the magnitude of the effect for the prototypical magnetoelectric material Cr$_2$O$_3$. We use low energy muon spin rotation to measure the strength of the magnetic field generated by charged muons as a function of their distance from the surface of a Cr$_2$O$_3$ films, and show that the results are consistent with the existence of the monopole. We discuss other possible routes to detecting the monopolar field, and show that, while the predicted monopolar field generated by Cr$_2$O$_3$ is above the detection limit for standard magnetic force microscopy, detection of the field using this technique is prevented by surface charging effects.
Two-dimensional topological materials (TMs) have a variety of properties that make them attractive for applications including spintronics and quantum computation. However, there are only a few such experimentally known materials. To help discover new 2D TMs, we develop a unified and computationally inexpensive approach to identify magnetic and non-magnetic 2D TMs, including gapped and semi-metallic topological classifications, in a high-throughput way using density functional theory-based spin-orbit spillage, Wannier-interpolation, and related techniques. We first compute the spin-orbit spillage for the ~1000 2D materials in the JARVIS-DFT dataset (https://www.ctcms.nist.gov/~knc6/JVASP.html ), resulting in 122 materials with high-spillage values. Then, we use Wannier-interpolation to carry-out Z2, Chern-number, anomalous Hall conductivity, Curie temperature, and edge state calculations to further support the predictions. We identify various topologically non-trivial classes such as quantum spin-hall insulators (QSHI), quantum anomalous-hall insulators (QAHI), and semimetals. For a few predicted materials, we run G0W0+SOC and DFT+U calculations. We find that as we introduce many-body effects, only a few materials retain non-trivial band-topology, suggesting the importance of high-level DFT methods in predicting 2D topological materials. However, as an initial step, the automated spillage screening and Wannier-approach provide useful predictions for finding new topological materials and to narrow down candidates for experimental synthesis and characterization.
The discovery of intrinsic magnetic topological order in $rm MnBi_2Te_4$ has invigorated the search for materials with coexisting magnetic and topological phases. These multi-order quantum materials are expected to exhibit new topological phases that can be tuned with magnetic fields, but the search for such materials is stymied by difficulties in predicting magnetic structure and stability. Here, we compute over 27,000 unique magnetic orderings for over 3,000 transition metal oxides in the Materials Project database to determine their magnetic ground states and estimate their effective exchange parameters and critical temperatures. We perform a high-throughput band topology analysis of centrosymmetric magnetic materials, calculate topological invariants, and identify 18 new candidate ferromagnetic topological semimetals, axion insulators, and antiferromagnetic topological insulators. To accelerate future efforts, machine learning classifiers are trained to predict both magnetic ground states and magnetic topological order without requiring first-principles calculations.
The correlations between the sequence of monomers in a polymer and its three-dimensional structure is a grand challenge in polymer science and biology. The properties and functions of macromolecules depend on their 3D shape that has appeared to be dictated by their monomer sequence. However, the progress towards understanding the sequence-structure-property correlations and their utilization in materials engineering are slow because it is almost impossible to characterize astronomically large number of possible sequences of a copolymer using traditional experimental and simulation methods. To address this problem, here, we combine evolutionary computing and coarse-grained molecular dynamics simulation and study the sequence-structure correlations of a model AB type copolymer system. The CGMD based evolutionary algorithm screens the sequence space of the copolymer efficiently and identifies wide range of single molecule structures including extremal radius of gyrations. The data provide new insights on the sequence-Rg correlations of the copolymer system and their impact on the structure and functionality of polymeric materials. The work highlights the opportunities of sequence specific control of macromolecular structure for designing materials with exceptional properties.
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