Superconductor and Critical Temperature Predictions Using Machine Learning

We used superconductors from the SuperCon database to perform

supervised machine learning to predict their critical temperatures. Using

only chemical composition as the predictor, our calculations

achieved a coefficient of determination R$^{2} \simeq 0.93$, which is

comparable to, and in some cases higher than, similar estimates using

other artificial intelligence techniques. Based on this machine

learning model, we predicted several new superconductors with

high critical temperatures. We also used unsupervised machine

learning to find possible clustering structure in the

superconducting materials data. Conventional clustering methods

like k-means, hierarchical or Gaussian mixtures, as well a clustering

method based on self-organizing maps (a type of artificial neural network),

were used. Our results indicate that machine learning can achieve,

and in some cases exceed, human level performance in clustering

superconductors.