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Register a new userToward a systematic discovery of artificial functional magnetic materials
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Although ferromagnets are found in all kinds of technological applications, only few substances are known to be intrinsically ferromagnetic at room temperature. In the past twenty years, a plethora of new artificial ferromagnetic materials have been found by introducing defects into non-magnetic host materials. In contrast to the intrinsic ferromagnetic materials, they offer an outstanding degree of material engineering freedom, provided one finds a type of defect to functionalize every possible host material to add magnetism to its intrinsic properties. Still, one controversial question remains: Are these materials really technologically relevant ferromagnets? To answer this question, in this work the emergence of a ferromagnetic phase upon ion irradiation is systematically investigated both theoretically and experimentally. Quantitative predictions are validated against experimental data from the literature of SiC hosts irradiated with high energy Ne ions and own experiments on low energy Ar ion irradiation of TiO$_2$ hosts. In the high energy regime, a bulk magnetic phase emerges, which is limited by host lattice amorphization, whereas at low ion energies an ultrathin magnetic layer forms at the surface and evolves into full magnetic percolation. Lowering the ion energy, the magnetic layer thickness reduces down to a bilayer, where a perpendicular magnetic anisotropy appears due to magnetic surface states.
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The discoveries of intrinsically magnetic topological materials, including semimetals with a large anomalous Hall effect and axion insulators, have directed fundamental research in solid-state materials. Topological quantum chemistry has enabled the understanding of and the search for paramagnetic topological materials. Using magnetic topological indices obtained from magnetic topological quantum chemistry (MTQC), here we perform a high-throughput search for magnetic topological materials based on first-principles calculations. We use as our starting point the Magnetic Materials Database on the Bilbao Crystallographic Server, which contains more than 549 magnetic compounds with magnetic structures deduced from neutron-scattering experiments, and identify 130 enforced semimetals (for which the band crossings are implied by symmetry eigenvalues), and topological insulators. For each compound, we perform complete electronic structure calculations, which include complete topological phase diagrams using different values of the Hubbard potential. Using a custom code to find the magnetic co-representations of all bands in all magnetic space groups, we generate data to be fed into the algorithm of MTQC to determine the topology of each magnetic material. Several of these materials display previously unknown topological phases, including symmetry-indicated magnetic semimetals, three-dimensional anomalous Hall insulators and higher-order magnetic semimetals. We analyse topological trends in the materials under varying interactions: 60 per cent of the 130 topological materials have topologies sensitive to interactions, and the others have stable topologies under varying interactions. We provide a materials database for future experimental studies and open-source code for diagnosing topologies of magnetic materials.
Transition metal oxides show fascinating physical properties such as high temperature superconductivity, ferro- and antiferromagnetism, ferroelectricity or even multiferroicity. The enormous progress in oxide thin film technology allows us to integrate these materials with semiconducting, normal conducting, dielectric or non-linear optical oxides in complex oxide heterostructures, providing the basis for novel multi-functional materials and various device applications. Here, we report on the combination of ferromagnetic, semiconducting, metallic, and dielectric materials properties in thin films and artificial heterostructures using laser molecular beam epitaxy. We discuss the fabrication and characterization of oxide-based ferromagnetic tunnel junctions, transition metal-doped semiconductors, intrinsic multiferroics, and artificial ferroelectric/ferromagetic heterostructures - the latter allow for the detailed study of strain effects, forming the basis of spin-mechanics. For characterization we use X-ray diffraction, SQUID magnetometry, magnetotransport measurements, and advanced methods of transmission electron microscopy with the goal to correlate macroscopic physical properties with the microstructure of the thin films and heterostructures.
The structural and magnetic anomaly of the layered compound SrFeO$_{2}$ were examined by first principles density functional calculations and Monte Carlo simulations. The down-spin Fe 3$d$ electron occupies the $d_{z^2}$ level rather than the degenerate ($d_{xz}$, $d_{yz}$) levels, which explains the absence of Jahn-Teller instability, the easy ab-plane magnetic anisotropy and the observed three-dimensional (0.5, 0.5, 0.5) antiferromagnetic order. Monte Carlo simulations show that the strong inter-layer spin exchange is essential for the high Neel temperature.
In spin-density-functional theory for noncollinear magnetic materials, the Kohn-Sham system features exchange-correlation (xc) scalar potentials and magnetic fields. The significance of the xc magnetic fields is not very well explored; in particular, they can give rise to local torques on the magnetization, which are absent in standard local and semilocal approximations. We obtain exact benchmark solutions for two electrons on four-site extended Hubbard lattices over a wide range of interaction strengths, and compare exact xc potentials and magnetic fields with approximations obtained from orbital-dependent xc functionals. The xc magnetic fields turn out to play an increasingly important role as systems becomes more and more correlated and the electrons begin to localize; the effects of the xc torques, however, remain relatively minor. The approximate xc functionals perform overall quite well, but tend to favor symmetry-broken solutions for strong interactions.
Regression machine learning is widely applied to predict various materials. However, insufficient materials data usually leads to a poor performance. Here, we develop a new voting data-driven method that could generally improve the performance of regression learning model for accurately predicting properties of materials. We apply it to investigate a large family (2135) of two-dimensional hexagonal binary compounds focusing on ferroelectric properties and find that the performance of the model for electric polarization is indeed greatly improved, where 38 stable ferroelectrics with out-of-plane polarization including 31 metals and 7 semiconductors are screened out. By an unsupervised learning, actionable information such as how the number and orbital radius of valence electrons, ionic polarizability, and electronegativity of constituent atoms affect polarization was extracted. Our voting data-driven method not only reduces the size of materials data for constructing a reliable learning model but also enables to make precise predictions for targeted functional materials.
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