Related papers: Machine learning modeling of superconducting criti…
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…
The magnetic properties of a material are determined by a subtle balance between the various interactions at play, a fact that makes the design of new magnets a daunting task. High-throughput electronic structure theory may help to explore…
We perform a large scale study of conventional superconducting materials using a machine-learning accelerated high-throughput workflow. We start by creating a comprehensive dataset of around 7000 electron-phonon calculations performed with…
We estimate a statistical model to predict the superconducting critical temperature based on the features extracted from the superconductor's chemical formula. The statistical model gives reasonable out-of-sample predictions: $\pm 9.5$ K…
We develop a multi-step workflow for the discovery of conventional superconductors, starting with a Bardeen Cooper Schrieffer inspired pre-screening of 1736 materials with high Debye temperature and electronic density of states. Next, we…
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)…
High-throughput computational and experimental design of materials aided by machine learning have become an increasingly important field in material science. This area of research has emerged in leaps and bounds in the thermal sciences, in…
Superconducting materials are of significant technological relevance for a broad range of applications, and intense research efforts aim at enhancing the critical temperature $T_{c}$. Intriguingly, while numerous studies have explored…
Technologies that function at room temperature often require magnets with a high Curie temperature, $T_\mathrm{C}$, and can be improved with better materials. Discovering magnetic materials with a substantial $T_\mathrm{C}$ is challenging…
We present materials informatics approach to search for superconducting hydrogen compounds, which is based on a genetic algorithm and a genetic programming. This method consists of four stages: (i) search for stable crystal structures of…
Ultrahigh lattice thermal conductivity materials hold great importance since they play a critical role in the thermal management of electronic and optical devices. Models using machine learning can search for materials with outstanding…
The melting temperature is important for materials design because of its relationship with thermal stability, synthesis, and processing conditions. Current empirical and computational melting point estimation techniques are limited in…
Metal superhydrides, known for their high hydrogen content and polyhedral hydrogen cages, are promising candidates for high-temperature superconductivity. Recent research has emphasized "chemical pre-compression," enabling hydrogen…
Recent research on compressed binary hydrides have unveiled the potential for achieving superconductivity at near-room-temperature. Nevertheless, the available decision-making procedures standing behind the selection of constituent elements…
Crystal-graph attention networks have emerged recently as remarkable tools for the prediction of thermodynamic stability and materials properties from unrelaxed crystal structures. Previous networks trained on two million materials…
High-temperature superconducting cuprates have the potential to be transformative in a wide range of energy applications. In this work we analyse the corpus of historical data about cuprates using materials informatics and re-examine how…
Cuprates, a member of high-Tc superconductors, have been on the long-debate on their superconducting mechanism, so that predicting the critical temperature of cuprates still remains elusive. Herein, using machine learning and first…
We employed a machine-learning assisted approach to search for superconducting hydrides under ambient pressure within an extensive dataset comprising over 150 000 compounds. Our investigation yielded around 50 systems with transition…
Two families of high temperature superconductors whose critical temperatures are higher than 50K are known. One is the copper oxides and the other is the iron-based superconductors. Comparisons of mechanisms between these two in terms of…
The surprising discovery of superconductivity in layered iron-based materials, with transition temperatures climbing as high as 55 K, has lead to thousands of publications on this subject over the past two years. While there is general…