The European Physical Journal B最新文献

A machine learning framework has been established to predict the thickness-dependent Spintronic Device characteristics (R_P: Parallel Resistance, R_AP: Antiparallel Resistance, TMR: Tunnel Magnetoresistance (%), STT-IN: In-plane Spin Transfer Torque, STT-OUT: Out of Plane STT) versus voltage behaviour for Organic Magnetic Tunnel Junction (MTJ) devices (x/Rubrene/Co, x = La₂O₃, LaMnO₃, La_0.7Ca_0.3MnO₃, La_0.7Sr_0.3MnO₃). Machine learning (ML) analysis reveals that variation in thickness and interfacial scattering strongly influence spintronic parameters. With proper hyperparameter tuning of the polynomial linear regression and support vector regression model, resistance profiles are well predicted for three MTJs, except La₂O₃. The spin-split band structure of La₂O₃ exhibits a higher density of electronic states near the Fermi level, which modifies the spin-dependent tunnelling behaviour; consequently, spin-flip-related transport requires more complex models. Optimised Gaussian process regression model with multiple kernels not only accurately predicts MTJ TMR responses across voltages and barrier thicknesses but also captures both simple and complex physical relationships arising from different physical effects. In La₂O₃, the ML model fails to capture in-plane and out-of-plane STT responses due to weak magnetic coupling between the electrodes, which abruptly enhances spin-damping compensation with changing barrier thickness. In contrast, varying model complexity in three MTJs, except La₂O₃, provides insights into underlying transport mechanisms, such as spin-flip scattering and spin-damping compensation. Our findings indicate that by leveraging ML approaches, unexplored TMR responses can be predicted for different thickness and voltage settings, when the transport physics of the MTJ are consistent. By utilising simulation and ML models, the study provides significant insights into achieving high TMR for next-generation memory, logic, and quantum technologies. The approach not only enables accurate prediction of MTJ performance but also reduces computational and experimental requirements, whilst simultaneously offering valuable information on device physics after visualising various parameters.

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Cesium fluoride (CsF), a prototypical ionic compound with a wide band gap, is of pivotal importance for numerous technological applications. However, its large band gap hinders its functional integration into optoelectronic devices requiring semiconductor properties. The aim of this work is to present a first-principles investigation of the structural, electronic, and dielectric properties of cubic CsF under hydrostatic pressure, using density functional theory within the generalized gradient approximation. Our findings demonstrate a progressive reduction of the band gap from 5.41 eV at ambient conditions to 2.40 eV at 105 GPa, associated with a transition from an indirect gap at zero pressure to a direct gap at high pressure. Analysis of the density of states reveals enhanced s–p hybridization between Cs 6s, and F 2p orbitals under compression, which drives the electronic evolution. In parallel, we observe a significant alteration of the dielectric properties with pressure. The Born effective charges and the electronic part of the dielectric tensor increase monotonically under compression, whereas the ionic part first decreases and then the trend reverses and grows slowly beyond 25 GPa, reflecting complex changes in lattice dynamics. These outcomes provide new insights into the pressure-dependent behavior of CsF and elucidate the potential of external compression as a tool to tune its electronic and dielectric properties for emerging functional applications.

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Meeting the global energy demand sustainably has established photocatalysis as a promising route for solar-driven hydrogen production. However, achieving large-scale hydrogen generation through this approach requires materials that are cost-effective, efficient, and stable enough to drive the water-splitting reaction. In this work, we computationally investigate bulk LaZO₃ perovskite oxides for photocatalytic application. The DFT-based calculated indirect band gaps (1.38–2.98 eV) exhibit conduction and valence band edges well aligned to overlap the water redox potentials, indicating suitability for photocatalytic water splitting. Furthermore, the effective mass analysis reveals favorable electron–hole mobility ratios (D = 1.19–4.73), suggesting efficient charge transport and reduced carrier recombination. Furthermore, the lower lattice thermal conductivity of LaZO₃ enhances charge separation and carrier lifetime, thereby improving its overall photocatalytic efficiency. This study establishes non-transition cations in La-based perovskite oxides as sustainable alternatives for solar water splitting.

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A NbTi superconducting dipole magnet was recently developed at the Institute of Modern Physics, Chinese Academy of Sciences, as a prototype coil for an advanced high-field facility. To assess its structural stability and performance throughout the whole operation process, both distributed optical fibers and embedded strain gauges were employed to monitor dynamic global strain distribution. The combination of embedded strain gauges and distributed optical fiber sensing technology provides a multi-dimensional, high-precision measurement method for the health detection of superconducting magnets. Both the global and single-point strains of the magnet system were monitored throughout the operating process, including the cooling and excitation phases. This combined approach establishes a layered monitoring pattern including specific point and distributed strain, facilitating a direct assessment of the overall uniformity of the coil structure and essential support for researching the design and electromagnetic characteristics of superconducting magnets.

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We searched cobalt and manganese silicides, germanide by crystal structure prediction method and ab initio theory. Five Co₃Si structures presenting lower energies than that of experimental P6₃/mmc are found, including one structure Cmcm that is 60 meV/atom lower than the correspondent P6₃/mmc. A lower-energy Co ({text{R}}overline{3} m) (− 7.03 eV/atom) is found, whose energy is higher than that of P6₃/mmc (− 7.04 eV/atom) but is lower than that of ({text{F}}moverline{3} m) (− 7.02 eV/atom). Three lower-energy Fe₅Si₃ structures are found, with energies of 30 meV/atom lower than that of experimental P6₃/mcm. The strong lattice shape dependence of magnetocrystalline anisotropy energy (MAE) is studied through X₅Si₃ (X = Mn, Fe, Co). The building-block shape and energy order of cobalt silicide is dominated by Co P6₃/mmc, ({text{F}}moverline{3} m), ({text{R}}overline{3} m), respectively. Several low-energy perfect or nearly-perfect easy-axis MAE for Mn₃Si, Mn₅Si₂, and Mn₅Si₃ structures are found, as are also the cases in Ge-containing counterparts. A structure I4₁22 with energy 300 meV/atom lower than that of experimental Mn₅Si₃ P6₃/mcm is found. One structure Mn₅Si₂ Cmc2₁ with giant MAE is found (E₀₀₁ = 728 and E₁₀₀ = 696 μeV/atom).

Graphical abstract

We searched binary cobalt silicides by structural prediction code. Five lower-energy structures with lower energies than that of metastable Co₃Si P6₃/mmc are searched, including a structure Cmcm with 60 meV/atom lower than that of P6₃/mmc. A lower-energy Co phase is predicted, ({text{R}}overline{3} m) (− 7.03 eV/atom), whose energy is higher than that of P6₃/mmc (− 7.04 eV/atom) but is lower than that of ({text{F}}moverline{3} m) (− 7.02 eV/atom).