The expansiveness of compositional phase space is too vast to fully search using current theoretical tools for many emergent problems in condensed matter physics. The reliance on a deep chemical understanding is one method to identify local minima of relevance to investigate further, minimizing sample space. Utilizing machine learning methods can permit a deeper appreciation of correlations in higher order parameter space and be trained to behave as a predictive tool in the exploration of new materials. We have applied this approach in our search for new high temperature superconductors by incorporating models which can differentiate structural polymorphisms, in a pressure landscape, a critical component for understanding high temperature superconductivity. Our development of a representation for machine learning superconductivity with structural properties allows fast predictions of superconducting transition temperatures (Tc) providing a r2 above 0.94.
@article{arxiv.2301.10474,
title = {Machine learning using structural representations for discovery of high temperature superconductors},
author = {Lazar Novakovic and Ashkan Salamat and Keith V. Lawler},
journal= {arXiv preprint arXiv:2301.10474},
year = {2023}
}