Structural and physical properties of the high-entropy alloy ${\left(\mathrm{NbTa}\right)}_{0.67}{\left(\mathrm{MoHfW}\right)}_{0.33}$ were studied by x-ray powder diffraction, energy dispersive x-ray spectroscopy, magnetization, electrical resistivity, and specific heat measurements. The experimental results were supported by theoretical calculations using two complementary approaches for electronic structure calculations: the Korringa-Kohn-Rostoker method with the coherent potential approximation (KKR-CPA) and projector augmented wave (PAW) within the density functional theory. It was found that the alloy forms with a cubic, body-centered structure (space group $Im\overline{3}m$, W-type structure) with a lattice parameter $a=3.287\left(1\right)$ Å and a random (but microscopically homogeneous) distribution of the constituent elements. At high temperature the alloy exhibits simple metallic behavior, while at low temperature it becomes a type-II superconductor with the critical temperature $T_{\mathrm{c}}\approx 4.3$ K and the upper critical field ${\mu }_0{H}_{\mathrm{c}2}\approx 1.45$ T. The electron-phonon coupling constant calculated from the PAW data, ${\lambda }_{\mathrm{el}-\mathrm{ph}}^{\mathrm{th}-\mathrm{PAW}}=0.63$, is in perfect agreement with the experimental results.

We propose an efficient method to analyze the influence of thermal fluctuations of a molecular junction on its electronic transport properties, and consequently, reliably predict the time-averaged value of the conductance at a finite temperature in the zero-bias regime. Our multiscale approach combines three complementary techniques, namely, large-scale quantum mechanics/molecular mechanics (QM/MM) molecular dynamics (MD) simulations, active machine learning methods, and nonequilibrium Green’s function transport calculations. Results for the exemplary Au(111)-S-C₆H₄-C₆H₄-S-Au(111) and Au(111)-N-C₆H₄-C₆H₄-N-Au(111) junctions indicate the substantial impact of the thermal evolution of the junction on its transport properties, which cannot be forecasted based just on the ground-state geometry. In the experimentally relevant temperature range around the room temperature, the predicted conductance values are 30–40% larger than those calculated for the minimum-energy configuration of the respective junction at 0 K.

The structural and physical properties of the new titanium- and niobium-rich type-A high-entropy alloy (HEA) superconductor Nb${}_{0.34}$Ti${}_{0.33}$Zr${}_{0.14}$Ta${}_{0.11}$Hf${}_{0.08}$ (in at.%) were studied by X-ray powder diffraction, energy dispersive X-ray spectroscopy, magnetization, electrical resistivity, and specific heat measurements. In addition, electronic structure calculations were performed using two complementary methods: the Korringa–Kohn–Rostoker Coherent Potential Approximation (KKR-CPA) and the Projector Augmented Wave (PAW) within Density Functional Theory (DFT). The results obtained indicate that the alloy exhibits type II superconductivity with a critical temperature close to 7.5 K, an intermediate electron–phonon coupling, and an upper critical field of 12.2(1) T. This finding indicates that Nb${}_{0.34}$Ti${}_{0.33}$Zr${}_{0.14}$Ta${}_{0.11}$Hf${}_{0.08}$ has one of the highest upper critical fields among all known HEA superconductors.

The rising demand for digital storage and environmental concerns necessitate ultra-high-density, energy-efficient solutions. Atomic-scale magnets (ASMs) based on transition metal (TM) dimers on defective graphene exhibit promising magnetic anisotropy energy (MAE) values, providing a robust barrier against magnetization reversal. However, identifying optimal TM-substrate configurations is challenging when relying solely on density functional theory (DFT) calculations with spin-orbit coupling. To address this, we developed a machine learning (ML) model trained on scalar-relativistic DFT data using a tree-based gradient boosting approach. Our model implicitly captures key physical interactions from second-order perturbation theory, ensuring reliable MAE predictions for systems beyond the training set. By bridging computational efficiency with interpretability, this work contributes to the development of ASMs for spintronics and quantum materials, offering a pathway to next-generation data storage technologies.