Journal of Physics: Condensed Matter最新文献

Quasi-one-dimensional hole gas is achievable in a semiconductor Ge nanowire. The lowest two subband dispersions of the hole gas are just two shifted parabolic curves with an anticrossing atkz=0. This peculiar low-energy subband structure manifests the existence of a strong 'spin' (pseudo spin)-orbit coupling. Based on the Luttinger-Kohn Hamiltonian in the axial approximation, we show two sets of combined dispersions that not only isolated from each other but also with strong 'spin'-orbit coupling are obtainable in the presence of strong magnetic field. Realistic calculations are performed for three representative nanowire growth directions [001], [111] and [110]. These results are further confirmed via constructing the low-energy effective Hamiltonian of the hole gas. We also calculate the external electric field induced spin splitting for comparison with the magnetic field induced spin splitting.

Predicting magnetic phase transitions traditionally relies on a Hamiltonian model to capture key magnetic interactions. Recent advances in machine learning enables the development of a unified approach that can handle diverse magnetic systems without designing new Hamiltonians for each case. To this end, we employ message-passing neural network (MPNN) potentials to investigate magnetic phase transitions of two-dimensional chromium trihalidesCrX3(X = I, Br, Cl) . We achieve this by introducing a specialized MPNN with the ability to incorporate the magnetic degrees of freedom. This magnetic MPNN incorporates atomic magnetic moments directly into the message-passing process, enabling accurate modeling of potential energy surfaces in magnetic materials. This approach improves on our previous work, which had the same aim but used Behler-Parrinello neural network that relies on hand-crafted descriptors as the underlying universal magnetic Hamiltonian. It also adds the capability to treat magnetic degrees of freedom and atom displacement in a unified way. Using two-dimensionalCrX3as examples and combining the MPNN with the Landau-Lifshitz-Gilbert equation, we simulate ferromagnetic and antiferromagnetic phase transitions as a function of temperature. These results highlight the potential of MPNNs for advancing research in magnetic materials.

The nature of magnetism in the cubic spinelCr0.1Mn0.9Fe0.2Co1.8O4is reported based on systematic investigations by means of magnetization (M), ac susceptibility (χ) and heat capacity (CP) measurements, as well as by neutron diffraction. Structural characterization of the sample was done usingx-ray absorption spectroscopy, neutron and synchrotron diffraction. TheMvs.Tvariation in different magnetic fields indicate ferrimagnetic ordering belowTC= 230 K, followed by a magnetic field and anisotropy induced spin reorientation atT_SR∼150 K. With increasingTstarting from 2 K, the coercivityHCand anisotropy fieldH_Kdecrease and become negligible forT>TSR. A model to explain theHCvs.Tdata shows thatT_SRis due to reorientation ofMalongHwhenH>HK. TheCPvs.Tdata shows a weakλ-type anomaly atTCwith changes in magnetic entropy smaller than those observed belowT_SRsuggesting that long-range magnetic ordering is completed belowT_SR. ForTSRJ_AA/kB= 7.9 K,J_AB/kB= 22.6 K andJ_BB/kB= -5.3 K) are determined from the temperature dependence of paramagnetic susceptibility forT>TC. This analysis also shows the low-spinS= 0 state of Co³⁺ions on theBsites which along with negligibleH_KforTSR

Doping graphene into copper monomers significantly enhances their mechanical properties, thereby broadening the application scope of graphene/copper nanocomposites. Molecular dynamics (MD) simulation serve as a powerful tool for investigating the mechanical behavior of these nanocomposites. This study systematically explores the influence of four critical factors-external temperature, graphene vacancy defects, graphene chirality, and insertion angle-on the performance of graphene/copper nanocomposites. However, the simultaneous analysis of these factors through MD simulations substantially escalates computational demands. To address the computational bottleneck of MD simulations in analyzing multifactorial interactions, we integrate LSTM networks and back propagation (BP) neural networks for dual-task prediction: (1) LSTM captures the complete tensile stress-strain behavior (300 time steps per case) by learning sequential MD data, and (2) BP networks predict Young's modulus and yield strength from critical parameters (temperature, chirality, vacancy defects). Results demonstrate that the LSTM model achievesR²= 0.96 for Young's modulus andR²= 0.94 for yield strength prediction, while the BP neural network further improves accuracy toR²= 0.97 for both properties. Notably, the LSTM model predicts the entire tensile process in 2.4 s per curve, reducing computational time by three orders of magnitude compared to MD simulations (typically requiring hours). Furthermore, LSTM effectively helps elucidate the whole tensile process of the composites, which enhances the ability to predict material properties.

We investigate the superconducting pairing symmetry in pressurized La₃Ni₂O₇based on a bilayer two-orbital model. There are two symmetric bandsαandγ, as well as two antisymmetric onesβandδ. It is found that theγband induces considerable ferromagnetic spin fluctuation and prefers an odd-frequency,s-wave spin triplet pairing state. The addition of the other bands gradually enhances some antiferromagnetic spin fluctuations which eventually surpass the ferromagnetic one. The superconducting pairing then evolves from a spin triplet into ad-wave spin singlet, and finally into ans±-wave one. The competition between thed-wave ands±-wave pairings relies on the relative strength of the ferromagnetic spin fluctuation to the antiferromagnetic ones.

Dynamical ean field theory (DMFT) is one of the powerful computational approaches to study electron correlation effects in solid-state materials and molecules. Its practical applicability is, however, limited by the quantity of numerical resources required for the solution of the underlying auxiliary Anderson impurity model. Here, the one-to-one mapping between electronic orbitals and the state of a qubit register suggests a significant computational advantage for the use of a quantum computer (QC) for solving this task. In this work we present a QC approach to solve a two-site DMFT model based on the variational quantum eigensolver (VQE) algorithm. We analyze the propagation of stachastic and device errors through the algorithm and their effects on the calculated self-energy. Therefore, we systematically compare results obtained on simulators with calculations on the IBMQ Ehningen QC hardware. We suggest a means to overcome unphysical features in the self-energy which already result from purely stochastic noise. Based heron, we demonstrate the feasibility to obtain self-consistent results of the two-site DMFT model based on VQE simulations with a finite number of shots.

Quantum chemical modeling of iridium oxide nanoparticles-(IrO2)n,n=1,2,3-adsorbed on (5, 5) carbon nanotubes (CNTs) is presented. Energetic, geometric, and electronic aspects have been analyzed in depth to understand the main features of the nanoparticles in the gas phase and the adsorption process involved. Covalent Ir-C bonding resulted from the interaction of the (IrO2)1and (IrO2)3particles with the CNT. To evaluate the performance of the material, the dissociation of water into H(ads)and OH(ads)has been investigated. Our results revealed that the intrinsic charge polarization of the iridium oxide clusters favors the water dissociation process, with low activation energies. Moreover, the nanoparticles remain stable and maintain covalent interactions with the CNT surface during the water dissociation process.

We calculate the non-linear charge currents in a two-dimensional electron system with Rashba (α) and Dresselhaus (β) linear spin-orbit interactions (SOIs) in the presence of in-plane electricEand magneticBfields. Working in a rotated system of coordinates that introducesα±βas effective couplings on perpendicular directions, we show that the currents, quadratic in the electric field, exist for all values of the chemical potentialµ, above or below the band crossing energyE₀. Our formalism uses a second order correction to the particle distribution functionδf(2)derived in a semi-classical approximation that takes into account the local change in the single-particle energy under the action of the electric field. In a quantum well, whereμ>E0, for a spin-orbit energy larger than the Zeeman splitting, the non-linear currents that flow perpendicular on the direction of the magnetic field are found to be proportional withα±βwhenE⊥Band with(α±β)2/(α∓β)whenE∥B, a result independent of the presence of any additional SOIs, such as the cubic Dresselhaus or warping.

The dynamics of quantum phase transitions of topological systems has attracted interest. Here we consider the effect of long-range hopping terms on the dynamic behavior. The long-range hoppings preserve chiral symmetry and decay as a function of distance, with an exponentβ. We also consider a situation where the hoppings are modulated by oscillations. We compare the results with those obtained for short-range models such as the Su-Schrieffer-Heeger model or its extension to second neighboring cells. While often the lowest critical times coincide with the first peak of the Loschmidt rate, several anomalous cases are identified. Also, the number of critical times often exceeds the absolute value of the change of the winding number in a quench. Various correlation functions are considered, and their time-dependence may often be used to identify the early-time dynamical phase transitions.

In this paper we showcase the strengths of polarized neutron imaging as a magnetic imaging technique through a case study on field-cooled multifilamentary YBCO tape carrying a transport current while containing a trapped magnetic field. The measurements were done at J-PARC's RADEN beamline, and the analysis is based on a radiograph of a single polarization component, to showcase the analysis potential for fast measurements with short acquisition time. Regions of internal damage are easily and accurately identified as the technique probes the internal magnetic field of the sample directly. Quantitative measurements of the integrated field strength in various regions are acquired using time-of-flight information. Finally, the strength of the screening currents in each filament of the superconductor are estimated by simulating an experiment with a model sample and comparing it to data. With this, we show that polarized neutron imaging is not only a useful tool for investigating magnetic structures but also for investigating currents.