Towards high-throughput superconductor discovery via machine learning

Abstract

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 single material or small subset of materials, and discoveries are made more or less serendipitously. Recent increases in computing power, novel machine learning algorithms, and improved experimental capabilities offer new opportunities to revolutionize superconductor discovery. These will enable the rapid prediction of structures and properties of novel materials in an automated, high-throughput fashion and the efficient experimental testing of these predictions. Here, we review efforts to use machine learning to attain this goal.

arXiv commentSubmitted to J. Phys. Cond. Matt. as part of a ROADMAP article on "Designing Room Temperature Superconductors"

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Cited by in corpus (79)

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• Quasi Two-dimensional Vortex Matter in ThH${}_{10}$ Superhydride
• $d$-wave superconductivity as a model for diborides apart MgB${}_2$
• Resonant phonons: Localization in a structurally ordered crystal
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• Superconductivity in metallic hydrogen
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