Related papers: A deep learning approach to search for superconduc…
Kogan et al. [Phys. Rev. B 80, 014507 (2009)] present a two-band model to investigate its effect on superconducting properties as, e.g., the superfluid density, the specific heat, the dependence of the superconducting transition temperature…
Thermoelectric materials can be used to construct devices which recycle waste heat into electricity. However, the best known thermoelectrics are based on rare, expensive or even toxic elements, which limits their widespread adoption. To…
Superconductivity in strongly correlated systems is a remarkable phenomenon that attracts a huge interest. The study of this problem is relevant for materials as the high Tc oxides, pnictides and heavy fermions. In this work we study a…
The basic features of multi-band superconductivity and its implications are derived. In particular, it is shown that enhancements of the superconducting transition temperature take place due to interband interactions. In addition, isotope…
Flat-band (FB) materials have emerged as promising platforms for exploring exotic quantum phases. While numerous candidates have recently been identified through spectroscopic techniques such as angle-resolved photoemission spectroscopy,…
We present a high-accuracy procedure for electronic structure calculations of strongly correlated materials. To address limitations in current electronic structure methods, we employ density functional theory in combination with the…
Modeling and simulating a power distribution network (PDN) for printed circuit boards (PCBs) with irregular board shapes and multi-layer stackup is computationally inefficient using full-wave simulations. This paper presents a new concept…
Accurate temperature measurement at the nanoscale is crucial for thermal management in next-generation microelectronic devices. Existing optical and scanning-probe thermometry techniques face limitations in spatial resolution, accuracy, or…
The electronic energy structures and magnetic properties of layered superconductors $R$Ni$_2$B$_2$C, $R$Fe$_4$Al$_8$ and FeSe are systematically studied, by using the density functional theory (DFT). The calculations allowed us to reveal a…
The effective attractive interaction between electrons, mediated by electron-phonon coupling, is a well-established mechanism of conventional superconductivity. In metals exhibiting a Fermi surface, the critical temperature of…
Theory of superconductivity in two-band non-adiabatic systems with strong electron correlations in the linear approximation over non-adiabaticity is built in the article. Having assumed weak electron-phonon interaction analytical…
Simulating electronic behavior in materials and devices with realistic large system sizes remains a formidable task within the $ab$ $initio$ framework due to its computational intensity. Here we show DeePTB, an efficient deep learning-based…
Redundancy and noise exist in the bands of hyperspectral images (HSIs). Thus, it is a good property to be able to select suitable parts from hundreds of input bands for HSIs classification methods. In this letter, a band attention module…
The affinity and specificity of protein-molecule binding directly impact functional outcomes, uncovering the mechanisms underlying biological regulation and signal transduction. Most deep-learning-based prediction approaches focus on…
In the last few years several ``universal'' interatomic potentials have appeared, using machine-learning approaches to predict energy and forces of atomic configurations with arbitrary composition and structure, with an accuracy often…
As semiconductor technologies continue to scale down to the nanoscale, the efficient prediction of material properties becomes increasingly critical. The tight-binding (TB) method is a widely used semi-empirical approach that offers a…
We present an ensemble machine-learning approach for composition-based, structure-agnostic screening of candidate superconductors among ternary hydrides under high pressure. Hydrogen-rich hydrides are known to exhibit high superconducting…
BaPd2As2 belongs to 122 pnictide group, famous for exhibiting superconductivity at high temperatures and other electronic correlations. Unlike the 122 iron arsenides, BaPd2As2 exhibits superconductivity at low temperature when realized in…
We introduce an approximate method which can be used to simulate the optical conductivity data of correlated multiband systems for normal and superconducting cases by taking advantage of a reverse process of a usual optical data analysis,…
The ionization energy theory is used to calculate the evolution of the resistivity and specific heat curves with respect to different doping elements in the recently discovered superconducting Pnictide materials. Subsequently, the…