Related papers: Machine learning modeling of superconducting criti…
One of the main goals and challenges of materials discovery is to find the best candidates for each interest property or application. Machine learning rises in this context to efficiently optimize this search, exploring the immense…
Predicting solid-solid phase transitions remains a long-standing challenge in materials science. Solid-solid transformations underpin a wide range of functional properties critical to energy conversion, information storage, and thermal…
The Eliashberg theory of superconductivity accounts for the fundamental physics of conventional electron-phonon superconductors, including the retardation of the interaction and the effect of the Coulomb pseudopotential, to predict the…
Machine Learning (ML) has offered innovative perspectives for accelerating the discovery of new functional materials, leveraging the increasing availability of material databases. Despite the promising advances, data-driven methods face…
The new family of unconventional iron-based superconductors discovered in 2006 immediately relieved their copper-based high-temperature predecessors as the most actively studied superconducting compounds in the world. The experimental and…
A review is given for the theoretical framework to give a reliable prediction of the superconducting transition temperature Tc from first principles, together with a practical strategy for its application to actual materials with…
Heat transfer characteristics of nanofluids have been extensively studied since the 1990s. Research investigations show that the suspended nanoparticles significantly alter the suspension's thermal properties. The thermal conductivity of…
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…
A wide variety of superconducting oxides are used to test a general model of high pressure induced transition temperature (T c) changes. The T c 's vary from a low of 24 K to a high of 164 K. Although the model is capable of predicting both…
We consider superconductivity in the light of the quantum gravity theoretical framework introduced in [1]. In this framework, the degree of quantum delocalization depends on the geometry of the energy distribution along space. This results…
Deep learning models were developed and implemented to aid the search for new heavy fermion compounds. For the purpose of these calculations a database of more than 200 heavy fermions was compiled from the literature. The deep learning…
The observation of superconductivity in hydride-based materials under ultrahigh pressures (for example, H$_3$S and LaH$_{10}$) has fueled the interest in a more data-driven approach to discovering new high-pressure hydride superconductors.…
We employ a descriptor based machine-learning approach to assess the effect of chemical alloying on formation-enthalpy of rare-earth intermetallics. Application of machine-learning approaches in rare-earth intermetallic design have been…
Knowledge of thermal properties is essential to design and evaluate thermal systems and processes using nanofluids. This paper presents different analytical models to predict thermal conductivity and viscosity. The efforts have been made to…
Irradiation of cuprate high-$T_c$ superconductors with light ions of moderate energy creates point defects that lead to a reduction or full suppression of the critical temperature. By shaping the ion flux with a stencil mask, nanostructures…
Iron-based superconductors, a cornerstone of low-temperature physics, have been the subject of numerous theoretical models aimed at deciphering their complex behavior. In this study, we present a comprehensive approach that amalgamates…
Leveraging strong optoelectronic responses to external stimuli, such as temperature and electric fields, is central to the development of advanced photonic technologies, including adaptive photodetectors and reconfigurable photovoltaic…
The adoption of machine learning in materials science has rapidly transformed materials property prediction. Hurdles limiting full capitalization of recent advancements in machine learning include the limited development of methods to learn…
A model, based on classical mechanics and thermodynamics and valid for \textit{all} superconductors, is devised to investigate the properties of the \textit{current-driven}, superconducting to normal transition. This process is shown to be…
Scaling relations between the superconducting transition temperature $T_{\rm c}$, the superfluid stiffness $\rho_{\rm s}$ and the normal state conductivity $\sigma_0(T_{\rm c})$ are identified within the class of molecular superconductors.…