相关论文: An Open and Collaborative Database of Properties o…

Reliable material property data is crucial for trustworthy simulations throughout different areas of engineering. Special care must be taken when materials at extreme conditions are under study. Superconductors and devices assembled from…

Data-driven methods, in particular machine learning, can help to speed up the discovery of new materials by finding hidden patterns in existing data and using them to identify promising candidate materials. In the case of superconductors,…

Materials aspects of cuprate high-temperature superconductors (HTS) are reviewed with respect to technical applications. The structural chemistry common to all HTS and their critical temperatures for the onset of superconductivity are…

Driven by the recent rapid increase in the number of materials databases published (open and commercial), I discuss here some perspectives on the growing need for standardized, interoperable, open databases. The field of computational…

Thermoelectric materials provide a sustainable way to convert waste heat into electricity. However, data-driven discovery and optimization of these materials are challenging because of a lack of a reliable database. Here we developed a…

The discovery of high-$T_c$ conventional superconductivity in high-pressure hydrides has helped establish computational methods as a formidable tool to guide material discoveries in a field traditionally dominated by serendipitous…

Low-temperature alloys are important for a wide spectrum of modern technologies ranging from liquid hydrogen, and superconductivity to quantum technology. These applications push the limit of material performance into extreme coldness,…

Materials databases built from calculations based on density functional approximations play an important role in the discovery of materials with improved properties. Most databases thus constructed rely on the generalized gradient…

High-pressure research is a productive route to new structures and emergent properties. However, crucial high-pressure structural information remains highly fragmented across individual publications and heterogeneous computational…

Inspired by nature, this study employs the Materials Genome Initiative to identify key components of HTSC superconductors. Integrating AI with high-throughput screening, we uncover crucial superconducting "genes". Through HTS techniques and…

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…

In this paper, we present a general review of the status of numerical modelling applied to the design of high temperature superconductor (HTS) devices. The importance of this tool is emphasized at the beginning of the paper, followed by…

The High-Throughput Experimental Materials Database (HTEM-DB) is the endpoint repository for inorganic thin-film materials data collected during combinatorial experiments at the National Renewable Energy Laboratory (NREL). This unique data…

Novel technologies and new materials are in high demand for future energy-efficient electronic devices to overcome the fundamental limitations of miniaturization of current silicon-based devices. Two-dimensional (2D) materials show…

HTS has the potential of a game changer for many applications of superconductivity, not last in the field of particle accelerators and detectors. This paper explores the potential of HTS, with a focus on REBCO-coated conductors, in relation…

As scientific discovery becomes increasingly data-driven, software platforms are needed to efficiently organize and disseminate data from disparate sources. This is certainly the case in the field of materials science. For example,…

The development of the high-temperature superconductors (HTS) conductors has allowed the development of diverse superconductor devices. Some of these devises, like the power generators and high-field magnets, are classified as large-scale…

The discovery of high-temperature superconducting materials holds great significance for human industry and daily life. In recent years, research on predicting superconducting transition temperatures using artificial intelligence~(AI) has…

A thorough in situ characterization of materials at extreme conditions is challenging, and computational tools such as crystal structural search methods in combination with ab initio calculations are widely used to guide experiments by…

Identifying novel functional materials with desired key properties is an important part of bridging the gap between fundamental research and technological advancement. In this context, high-throughput calculations combined with data-mining…