{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/machine-learning-using-structural","title":"Machine learning using structural representations for discovery of high temperature superconductors","arxiv_id":"2301.10474","date":"2023-01-25","proceeding":null,"authors":["Lazar Novakovic","Ashkan Salamat","Keith V. Lawler"],"abstract":"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 ($T_c$) providing a $r^2$ above 0.94.","url_abs":"https://arxiv.org/abs/2301.10474v1","url_pdf":"https://arxiv.org/pdf/2301.10474v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"machine-learning-using-structural","repo_url":"https://github.com/lazarnov/superconductor-predict","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}