Related papers: Machine learning modeling of superconducting criti…
Based on experimental results and our previous theoretical work, a microscopic theory of high temperature superconductivity is conjectured. In this conjecture, superconducting and antiferromagnetic long-range orders are driven by interlayer…
Databases compiled using ab-initio and symmetry-based calculations now contain tens of thousands of topological insulators and topological semimetals. This makes the application of modern machine learning methods to topological materials…
A deep neural network was developed for the purpose of predicting thermal conductivity with a case study performed on neutron irradiated nuclear fuel. Traditional thermal conductivity modeling approaches rely on existing theoretical…
The prediction of transition temperatures can be regarded in several ways, either as an exacting test of theory, or as a tool for identifying theoretical rules for defining new homology models. Popular "first principle" methods for…
We investigated the material parameters of several single-layer cuprates, including those with fluorinated buffer layers, with the aim of identifying possible high-temperature superconductors. To evaluate the material parameters, we use the…
The goal of most materials discovery is to discover materials that are superior to those currently known. Fundamentally, this is close to extrapolation, which is a weak point for most machine learning models that learn the probability…
Room temperature superconductivity, as one of the famous jewels on the crown of physics, has attracted continuous attention and unremitting efforts from numerous scientists. In recent years, more and more reports on room temperature…
Study of superconductivity in layered iron-based materials was initiated in 2006 by Hosono's group, and boosted in 2008 by the superconducting transition temperature, Tc, of 26 K in LaFeAsO1-xFx. Since then, enormous researches have been…
Searching for natural materials exhibiting larger electron-electron interactions constitutes a traditional approach to high temperature superconductivity research. Very recently we pointed out that the newly developed field of…
Lattice thermal conductivity (TC) of semiconductors is crucial for various applications, ranging from microelectronics to thermoelectrics. Data-driven approach can potentially establish the critical composition-property relationship needed…
I present a selection of experimental results on metallic cuprates, both above the superconducting transition temperature $T_c$ (often called the strange metal state) and in the superconducting state. It highlights this still poorly…
Transforming CO$_2$ into methanol represents a crucial step towards closing the carbon cycle, with thermoreduction technology nearing industrial application. However, obtaining high methanol yields and ensuring the stability of…
Machine Learning (ML) has impacted numerous areas of materials science, most prominently improving molecular simulations, where force fields were trained on previously relaxed structures. One natural next step is to predict material…
Room temperature superconductivity remains elusive, and hydrogen-base compounds despite remarkable transition temperatures(Tc) typically require extreme pressures that hinder application. To accelerate discovery under moderate pressures, an…
Machine learning (ML) has emerged as a powerful tool for accelerating the computational design and production of materials. In materials science, ML has primarily supported large-scale discovery of novel compounds using first-principles…
Superconductivity at low temperature -- observed in lithium and bismuth, as well as in various low-density superconductors -- calls for developing reliable theoretical and experimental tools for predicting ultralow critical temperatures,…
Heusler compounds have always attracted a great deal of attention from researchers thanks to a wealth of interesting properties for technological applications. They are intermetallic ductile compounds, and some of them have been found to be…
The impurity concentration dependence of superconducting transition temperature $T_c$ is studied within the framework of two band model of order parameter anisotropy. Both intraband and interband electron scattering by nonmagnetic and…
Owing to its high scalability and computational efficiency, machine learning methods have been increasingly integrated into various scientific research domains, including ab initio-based materials design. It has been demonstrated that, by…
In the field of Artificial Intelligence (AI) and Machine Learning (ML), the approximation of unknown target functions $y=f(\mathbf{x})$ using limited instances $S={(\mathbf{x^{(i)}},y^{(i)})}$, where $\mathbf{x^{(i)}} \in D$ and $D$…