The European Physical Journal B最新文献

We investigate the nonlinear dynamics of the deterministic and stochastic Hindmarsh–Rose (HR) neuron models under the influence of an external sinusoidal electric current and magnetic flow effects. In the deterministic regime, in the absence of a magnetic field, we observe multistability between periodic and chaotic attractors. This is accompanied by the emergence of self-similar windows of chaotic dynamics that converge within a broad domain of periodic dynamics in parameter space. Introducing a magnetic flux partially suppresses chaotic dynamics while maintaining multistability. Under stochastic conditions due to the introduction of Gaussian noise with arbitrarily small intensity, D, noise triggers transitions between coexisting states, exhibiting a preference for specific attractors from the deterministic case without returning to any other coexisting metastable states. By increasing D and appropriately adjusting the remaining control parameters of the HR neuron model, it becomes feasible to achieve regimes of noise-induced chaos or noise-induced stabilization, effectively suppressing chaotic dynamics. Furthermore, within this framework, we explore the existence of transient chaotic dynamics.

In this study, we delve into the realm of solving the Schrödinger equation within spherical quantum dots (QDs) characterized by arbitrary potentials, leveraging the capabilities of machine learning methodologies. Our approach involves training neural networks (NNs) through a curated collection of potentials and wave functions (WFs), which were initially computed using the classical finite element method. To gauge the reliability of the estimates produced by these NNs, we introduce accuracy indicators for rigorous assessment. The training procedure relies on the gradient descent method to optimize the networks’ performance. Furthermore, our investigation encompasses scenarios with analytical potentials, broadening the scope of our analysis beyond empirical cases. By integrating analytical potentials into our study, we aim to achieve a comprehensive understanding of the neural network’s effectiveness in handling various potential profiles. This expansion opens avenues for more versatile and insightful quantum mechanical explorations within the realm of nanoscale systems. Among the findings, the QD core exhibited the highest level of accuracy in WF estimation, achieved through the utilization of a spherical potential. Conversely, the estimation performance was least reliable in scenarios involving HLP, with a notable deviation of 16.68%. Transitioning to the core/shell structure, employing the double HLP configuration resulted in the most precise estimation of WFs. This contrasts significantly with the estimation performance for the V-Shaped Potential, where accuracy was comparatively lower with deviation of 4%.

Graphical Abstract

In this work, we solved the Schrödinger equation within spherical quantum dots characterized by arbitrary potentials, leveraging the capabilities of machine learning methodologies. Our approach includes training neural networks through a curated collection of potentials and wave functions, which were initially computed using the classical finite element method. By integrating analytical potentials into our study, we aim to achieve a comprehensive understanding of the neural network’s effectiveness in handling various potential profiles. The estimation performance was least reliable in scenarios involving HLP, with a notable deviation of 16.68%. Transitioning to the core/shell structure, employing the double HLP configuration resulted in the most precise estimation of WFs. This contrasts significantly with the estimation performance for the V-Shaped Potential (VP), where accuracy was comparatively lower with deviation of 4%

Classical molecular dynamics simulations are used to study structural properties of a planar interface between amorphous silica and sodium tetrasilicate glass in a dry environment. The interface region is characterised by the change of the network connectivity. The effect of the interfacial microstructure on the diffusion of sodium is analysed within the framework of topological constraint theory. It is shown that the interface represents an elastic phase boundary where a transition to a stressed-rigid phase occurs. The onset of rigidity in the interface region makes the out-diffusion of sodium from the sodium silicate glass more difficult. The topological controlled diffusion kinetics provides an explanation for the barrier property of amorphous silica despite its open network structure.

Graphical abstract

The influence of dissipation with viscosity on magnetohydro-convective instability in a saturated Darcy–Brinkman medium is examined. The bottom boundary is designated as adiabatic, whereas the top boundary is isothermal. Numerical linear stability analysis investigates normal modes that disturb the horizontal base flow at different inclinations. The case study shows that the most unstable disturbances are horizontal rolls, normal modes characterized by a wave vector perpendicular to the main flow direction. The horizontal rolls are the favored instability mode. Barletta et al. also showed that horizontal rolls are more unstable than any other oblique roll mode in the hydromagnetic scenario. This finding provides insights into the behavior of MHD fluid flow and heat transfer in porous media, with implications for applications in geoscience, engineering, and environmental science.

Graphical abstract

This study employs density functional theory, utilizing plane wave ultra-soft pseudopotentials (PW-USPPs) within the generalized gradient approximation (GGA) and incorporating the Hubbard potential (GGA + U), to investigate the structural, electronic, and magnetic characteristics of Mn-doped 2D—WSe₂ monolayers. The pristine WSe₂ monolayer is identified as a nonmagnetic direct band gap semiconductor with a band gap of 1.55 eV. Upon substituting Mn for W in the WSe₂ monolayer, the resulting structure exhibits enhanced stability, indicated by a negative formation energy. The Mn-doped monolayer-WSe₂ adopts a semi-metallic nature, deviating from the nonmagnetic characteristics of the pristine 2D—WSe₂, showcasing impurity bands dispersion near the Fermi level. The total magnetic moment within the nearest neighboring interactions of the Mn impurity atoms increases notably and it is attributed to the electron-correlation effect in the high spin state under the GGA + U approximation. However, this correlation effect proves insignificant on the total magnetic moment for the second nearest neighboring interactions, yielding consistent outcomes in both GGA and GGA + U approximations. The transition of ferromagnetic to antiferromagnetic states occurs within the temperature range of 400 K–450 K for low Mn-atom concentrations (below 12.5%), indicating long-range ferromagnetic ordering crucial for high-temperature 2D-diluted magnetic semiconductors. However, at high concentrations of impurity atoms, the temperature drops below room temperature (220 K in 22.2%) in the doped monolayer WSe₂, showing weak magnetic interaction. Lastly, the optical properties of the doped system is studied by applying polarization in perpendicular and parallel directions to the plane of the monolayer.

Graphical abstract