Towards high-throughput superconductor discovery via machine learning

TL;DR

This paper reviews how machine learning can revolutionize superconductor discovery by enabling rapid, automated prediction and testing of new materials, moving beyond traditional serendipitous methods.

Contribution

It provides a comprehensive overview of recent efforts to apply machine learning for high-throughput superconductor discovery, highlighting advancements and future prospects.

Findings

Machine learning enables rapid prediction of superconductor properties.

High-throughput methods can accelerate material discovery.

Machine learning integration improves experimental efficiency.

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.