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Recent advances in 2D magnetism have heightened interest in layered magnetic materials due to their potential for spintronics. In particular, layered semiconducting antiferromagnets exhibit intriguing low-dimensional semiconducting behavior with both charge and spin as carrier controls. However, synthesis of these compounds is challenging and remains rare. Here, first-principles based high-throughput search is conducted to screen potentially stable mixed metal phosphorous trichalcogenides (MM ' P 2 X 6 , where M and M ' are transition metals and X is a chalcogenide) that have a wide range of tunable bandgaps and interesting magnetic properties. Among the potential candidates, a stable semiconducting layered magnetic material, CdFeP 2 Se 6 , that exhibits a short-range antiferromagnetic order at T N = 21 K with an indirect bandgap of 2.23 eV is successfully synthesized . This work suggests that high-throughput screening assisted synthesis can be an effective method for layered magnetic materials discovery.
Spintronic elements based on spin transfer torque have emerged with potential for on-chip memory, but they suffer from large energy dissipation due to the large current densities required. In contrast, an electric-field-driven magneto-electric storage element can operate with capacitive displacement charge and potentially reach 1-10 µJ cm -2 switching operation. In this paper, magneto-electric switching of a magnetoresistive element is shown, operating at or below 200 mV, with a pathway to get down to 100 mV. A combination of phase detuning is utilized via isovalent La substitution and thickness scaling in multiferroic BiFeO 3 to scale the switching energy density to ≈10 µJ cm -2 . This work provides a template to achieve attojoule-class nonvolatile memories.
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The investigation of finite temperature properties using Monte-Carlo (MC) methods requires a large number of evaluations of the system’s Hamiltonian to sample the phase space needed to obtain physical observables as function of temperature. DFT calculations can provide accurate evaluations of the energies, but they are too computationally expensive for routine simulations. To circumvent this problem, machine-learning (ML) based surrogate models have been developed and implemented on high-performance computing (HPC) architectures. In this paper, we describe two ML methods (linear mixing model and HydraGNN) as surrogates for first principles density functional theory (DFT) calculations with classical MC simulations. These two surrogate models are used to learn the dependence of target physical properties from complex compositions and interactions of their constituents. We present the predictive performance of these two surrogate models with respect to their complexity while avoiding the danger of overfitting the model. An important aspect of our approach is the periodic retraining with newly generated first principles data based on the progressive exploration of the system’s phase space by the MC simulation. The numerical results show that HydraGNN model attains superior predictive performance compared to the linear mixing model for magnetic alloy materials.
This study reports a systematic density functional theory-based analysis on electronic properties of individual doping and codoping of Ti and Ce into GaN. In the case of codoped GaN, the placement of Ti and Ce at nearest neighbour sites appeared as the most stable configuration. The unoccupied states in the case of individual doped material are observed to become occupied after codoping, and a metal-to-metal charge transfer of the Ti-N-Ce character is observed in the material. The interaction between dopants suggests 4f-5d-CB hybridization that opens the way to the exploitation of novel luminescence phenomena. A model showing charge transfer and interaction between the dopants is proposed.
Non-equilibrium magnetic properties are studied in a two-dimensional mixed spin (1, 5/2) Blume-Capel Ising system under the influence sinusoidal magnetic fields using the effective-field theory based on a decoupling approximation. A dynamic equation of motion has been solved for a square lattice by utilizing a Glauber-type stochastic process. Dynamic phase diagrams are presented in the reduced magnetic field amplitude and reduced temperature plane (h,T) and reduced crystal-field and reduced temperature plane (Δ,T). We observe that the system exhibits dynamic tricritical behaviors. Phase diagrams contain the paramagnetic (p), ferrimagnetic (i), and coexistence or mixed-phase regions, namely the i,p, and i + p, which strongly depend on the crystal-field interaction parameter and oscillating magnetic field frequency. We have found that the system only exhibits a second-order transition line in the higher-frequency region. We have also compared our result with the mean-field prediction in order to indicate the effects of correlations and found that some of the dynamic first-order phase lines disappeared.
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The structure and magnetic properties of Nd(Al 1-x Fe x ) 2 alloys with x = 0, 0.05, 0.1 and 0.2, which adopt cubic MgCu 2 -type structure with space group Fd3¯m, are studied using x-ray powder diffraction, static and dynamic magnetization, and calorimetric measurements. The lattice constant decreases linearly with increasing Fe content from 8.0106(4) Å to 7.9044(4) Å. Large thermal irreversibilities between zero-field-cooled and field-cooled conditions are observed for all substituted samples. Here, the magnetic transition temperatures exhibit a minimum for x = 0.1, and coercive fields at 2 K increase from 70 Oe for x = 0 to 6.3 kOe for x = 0.2. The absence of λ-type peak in the heat capacity data, frequency dependence of ac magnetic susceptibility, and memory effects indicate formation of a cluster-glass magnetic state when x ≥ 0.1.
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