Related papers: Critical Temperature Prediction for a Superconduct…
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…
We develop a transferable machine learning model which predicts structural relaxation from amorphous supercooled liquid structures. The trained networks are able to predict dynamic heterogeneity across a broad range of temperatures and time…
A long-standing problem of observing Room Temperature Superconductivity is finally solved by a novel approach. Instead of increasing the critical temperature Tc of a superconductor, the temperature of the room was decreased to an…
Predicting the Curie temperature ($T_\mathrm{C}$) of magnetic materials is crucial for advancing applications in data storage, spintronics, and sensors. We present a machine learning (ML) framework to predict $T_{\mathrm{C}}$ using a…
Accurate thermal analysis of composites and porous media requires detailed characterization of local thermal properties in small scale. For some important applications such as lithium-ion batteries, changes in the properties during the…
Quantum condensation is used here as the basis for a phenomenological theory of superfluidity and superconductivity. It leads to remarkably good calculations of the transition temperatures T c of superfluid 3 He and 4 He, as well as a large…
Even though superconductivity has been studied intensively for more than a century, the vast majority of superconductivity research today is carried out in nearly the same manner as decades ago. That is, each study tends to focus on only a…
We discuss an aspect of neural networks for the purpose of phase transition detection. To this end, we first train the neural network by feeding Ising/Potts configurations with labels of temperature so that it can predict the temperature of…
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…
To synthesize a new superconductor which has a critical temperature, Tc, exceeding the room temperature, one needs to know what chemical components to start with. This chapter presents analysis of experimental data which allow one to draw a…
Magnetic cooling based on the magnetocaloric effect is a promising solid-state refrigeration technology for a wide range of applications in different temperature ranges. Previous studies have mostly focused on near room temperature (300 K)…
Molecular dynamics simulations have been extensively used to predict thermal properties, but simulating different phases with similar precision using a unified force field is often difficult, due to the lack of accurate and transferrable…
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…
Since model bias and associated initialization shock are serious shortcomings that reduce prediction skills in state-of-the-art decadal climate prediction efforts, we pursue a complementary machine-learning-based approach to climate…
High critical temperature (T$_c$) superconductor has a great potential in many industrial applications. However, discovering a compound having high T$_c$ is still remaining a big challenge for experimental approach due to time-consuming and…
Near a two-dimensional Ising-type nematic quantum critical point, the quantum fluctuations of the nematic order parameter are coupled to the electrons, leading to non-Fermi liquid behavior and unconventional superconductivity. The interplay…
By using multi-bands BCS theory, we have calculated the superconductivity energy gap and the critical temperature of a thin-film metallic superconductor. The thermodynamic superconducting characteristics such as critical magnetic field,…
The superconducting transition in High Temperature Superconductors(HTS) has not been understood. Traditional Bose Einstein Condensation(BEC) theory provides Tc estimates that are many times higher than the experimental values. The proposed…
Artificial Neural Networks are connectionist systems that perform a given task by learning on examples without having prior knowledge about the task. This is done by finding an optimal point estimate for the weights in every node.…
The description of superconductivity at high temperature is a problem that has recently been addressed. Transition temperature of superconductivity, $T_c$, depends on the lattice structure type, size, and room pressure. In super-lattices…