We investigated the thermoelectric effects of the Dirac electron system Ag2Te under magnetic field. Our anal-ysis based on the Boltzmann semiclassical model associated the disorder with the unconventional magnetic field responses such as linear magnetoresistance, linear Nernst effect, step-like Nernst effect, and sign change in Nernst effect. The analysis also revealed the impurity band near the Fermi energy. We simultaneously clari-fied the serious impact of the thermal Hall effect on the measurement of the Nernst effect, and we proposed the definitive solution. Our careful measurement and analysis will be the standard for the thermoelectric study under magnetic field.
FeSe stands out among iron-based superconductors due to its extended nematic phase without the onset of long-range magnetic order. While strain-dependent electrical resistivity has been extensively explored to probe nematicity, its influence on magnetotransport properties remains less understood. In this work, we present measurements of the magnetoelastoresistivity in FeSe as a function of temperature and applied magnetic field. Using a minimal multiband Boltzmann model for transport we derive analytical expressions that capture the magnetic behavior of the whole set of experimental data both in the paramagnetic and in the nematic phase. These findings indicate that a multiband framework can robustly describe the magnetoelasto-transport properties in FeSe and arguably in other iron-based superconductors.
Image-potential surface states are simple model systems, where spin-dependent effects can be studied in view of spintronic applications. The Rashba effect in surface states at high- Z materials is related to both the strong spin-orbit interaction in heavy atoms and the orbital angular momentum arising from the inversion-symmetry breaking at the surface. Our study on image-potential states at Bi 2 Se 3 and Bi 2 Te 3 showcases that the orbital angular momentum causes an intrinsically reversed Rashba parameter α R ≈ − 100 meV Å , i.e., a reversed spin splitting compared with the prototypical Rashba-split Au(111) crystal-induced surface state. We present a consistent picture of spin-resolved experimental data from inverse photoemission and three-photon photoemission together with calculations from density-functional theory and many-body perturbation theory.
We report a machine-learned interatomic potential for uranium dioxide with xenon gas, as well as a machine-learned potential for uranium dioxide. Training datasets were constructed by leveraging a combination of density functional theory calculations with a Hubbard U correction and molecular dynamics simulations. Query-by-committee active learning procedures further automated the augmentation of training datasets. The efficacy of employing an equivariant message-passing neural network for iterative potential fitting was demonstrated by reproducing DFT + U -level forces and energies, despite the training datasets being much smaller than those for recently reported uranium dioxide MLPs. We found that our machine-learned potential for UO 2 achieves strong agreement with experimentally observed thermophysical and thermomechanical properties across a temperature range 300–3000 K. Our second machine-learned potential for uranium dioxide with xenon successfully replicates reference DFT + U incorporation energies of xenon into various lattice sites. We also employed this MLP to calculate migration barriers for xenon diffusion via tetravacancy defect cluster mechanisms. The superlative ability of these machine-learned potentials to capture the behavior of uranium dioxide with xenon inclusion across a range of temperatures and defect chemistries lays the foundation for larger-scale molecular dynamics simulations of fission gas transport through uranium oxide fuel matrices.
