Related papers: Machine learning of superconducting critical tempe…
This paper introduces the Julia package IsoME, an easy-to-use yet accurate and robust computational tool designed to calculate superconducting properties. Multiple levels of approximation are supported, ranging from the basic…
The ab initio calculations suggest (Y. Ma et al.), that the high-pressure ($p=200$~GPa) superconducting state in $\rm{BiH_{5}}$ and $\rm{BiH_{6}}$ compounds characterizes with a high value of the critical temperature ($T_{C}\sim 100$~K).…
We discuss the possibility of superconductivity in graphene taking into account both electron-phonon and electron-electron Coulomb interactions. The analysis is carried out assuming that the Fermi energy is far away from the Dirac points,…
The discovery of a record high superconducting transition temperature (Tc) of 288 K in a pressurized hydride inspires new hope to realize ambient condition superconductivity. Here, we give a perspective on the theoretical and experimental…
Searching for superconducting hydrides has so far largely focused on finding materials exhibiting the highest possible critical temperatures ($T_c$). This has led to a bias towards materials stabilised at very high pressures, which…
The phonon contribution to the thermal conductivity at low temperature in superconductors with line nodes is calculated assuming that scattering by both nodal quasiparticles and the sample boundaries is significant. It is determined that,…
Internal energies, enthalpies, phonon dispersion curves, and superconductivity of atomic metallic hydrogen are calculated. The (standard) use pseudopotentials in density-functional theory are compared with full (Coulomb)-potential…
A random forest regression based supervised machine learning method to predict experimental critical temperature of superconductivity from the electronic band structure, as obtained from Density Functional Theory, is demonstrated. This…
We use the experimental technique of thermal modulation reflectometry to study the relatively small temperature dependence of the optical conductivity of superconductors. Due to a large cancellation of systematic errors, this technique is…
The recent discovery of superconductivity in oxypnictides with the critical temperature (TC) higher than McMillan limit of 39 K (the theoretical maximum predicted by Bardeen-Cooper-Schrieffer (BCS) theory) has generated great excitement.…
Over the past several years, there have been many studies demonstrating the ability of deep neural networks to identify phase transitions in many physical systems, notably in classical statistical physics systems. One often finds that the…
We present an efficient criterion for probing the critical temperature of hydrogen based superconductors. We start by expanding the applicability of 3D descriptors of electron localization to superconducting states within the framework of…
We perform Eliashberg calculations for magnon-mediated superconductivity in a normal metal, where the electron-magnon interaction arises from interfacial coupling to antiferromagnetic insulators. In agreement with previous studies, we find…
We develop an artificial neural network model to predict quantum heat engines working within the experimentally realized framework of electromagnetically induced transparency. We specifically focus on {\Lambda}-type alkali-based cold atomic…
In the pursuit of developing high-temperature alloys with improved properties for meeting the performance requirements of next-generation energy and aerospace demands, integrated computational materials engineering (ICME) has played a…
We study the thermodynamic properties of impure superconductors by explicitly taking into consideration the finiteness of electronic bandwidths within the phonon-mediated Eliashberg formalism. For a finite electronic bandwidth, the…
The quest to identify new superconducting materials with enhanced properties is hindered by the prohibitive cost of computing electron-phonon spectral functions, severely limiting the materials space that can be explored. Here, we introduce…
We present a machine learning (ML) method for efficient computation of vibrational thermal expectation values of physical properties from first principles. Our approach is based on the non-perturbative frozen phonon formulation in which…
In the paper, the thermodynamic properties of the superconducting state in the $\rm{B_{2}H_{6}}$ compound have been characterized. The pressure of 360 GPa has been taken into account. The calculations have been carried out in the framework…
Predicting high temperature superconductors has long been a great challenge. A major difficulty is how to predict the transition temperature Tc of superconductors. Recently, progress in material informatics has led to a number of machine…