Recent advances in high-throughput superconductivity research

Abstract

Superconducting materials find applications in a rapidly growing number of technological areas, and searching for novel superconductors continues to be a major scientific task. However, the steady increase in the complexity of candidate materials presents a big challenge to the researchers in the field. In particular, conventional experimental methods are not well suited to efficiently search for candidates in compositional space exponentially growing with the number of elements; neither do they permit quick extraction of reliable multidimensional phase diagrams delineating the physical parameters that control superconductivity. New research paradigms that can boost the speed and the efficiency of superconducting materials research are urgently needed. High-throughput methods for rapid screening and optimization of materials have demonstrated their utility for accelerating research in bioinformatics and pharmaceutical industry, yet remain rare in quantum materials research. In this paper, we will briefly review the history of high-throughput research paradigm and then focus on some recent applications of this paradigm in superconductivity research. We consider the role these methods can play in all stages of materials development, including high-throughput computation, synthesis, characterization, and the emerging field of machine learning for materials. The high-throughput paradigm will undoubtedly become an indispensable tool of superconductivity research in the near future.

Overcoming Phase‐Purity Challenges in Complex Metal Oxide Photoelectrodes: A Case Study of CuBi2O4

“…Examples of this are found in the field of high-temperature metal oxide superconductors, which showed an increased transition temperature with an increasing number of elements. [17,18] This exciting prospect has generated extensive high-throughput and combinatorial efforts to accelerate the discovery and study of new complex metal oxides photo absorbers for solar water splitting. [19][20][21][22][23][24] However, with rapidly increasing efforts toward finding new ideal compositions of photoabsorbers with enhanced properties, the number of different cations is very probable to increase.…”

Predicting new superconductors and their critical temperatures using machine learning

“…In recent years, there has been a surge of interest in using artificial intelligence (AI), in particular machine learning (ML) and deep learning (DL), in materials physics [3][4][5] . The idea is that by using the existing information in materials' databases, one can predict new materials with certain desired properties.…”

Artificial intelligence for search and discovery of quantum materials

“…One proven platform to effectively host such experiments is the high-throughput approach, which permits the interrogation of libraries that contain hundreds or even thousands of different compounds 111 . Over the years combinatorial approaches have been effectively used to explore new superconductors 112 and map their compositional phase diagrams (for an example demonstrating the ability of this method to discover superconducting compositions see Fig. 4a).…”