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.
Keywords
Cite
@article{arxiv.2104.11150,
title = {Towards high-throughput superconductor discovery via machine learning},
author = {Stephen R. Xie and Y. Quan and Ajinkya Hire and Laura Fanfarillo and G. R. Stewart and J. J. Hamlin and R. G. Hennig and P. J. Hirschfeld},
journal= {arXiv preprint arXiv:2104.11150},
year = {2021}
}
Comments
Submitted to J. Phys. Cond. Matt. as part of a ROADMAP article on "Designing Room Temperature Superconductors"