{"id":"doi:10.1039/c8me00012c","kind":"article","title":"Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery","authors":["Bryce Meredig","Erin Antono","Carena Church","Maxwell Hutchinson","Julia Ling","Sean P. Paradiso","Ben J. Blaiszik","Ian T Foster","Brenna M. Gibbons","Jason R. Hattrick-Simpers","Apurva Mehta","Logan T. Ward"],"author_count":12,"year":2018,"published":"2018-01-01","venue":"Molecular Systems Design & Engineering","cited_by":268,"doi":"10.1039/c8me00012c","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1039/c8me00012c","archive_url":"https://space-kitty.com/papers/doi:10.1039/c8me00012c","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W2885048850","pdf_url":"https://pubs.rsc.org/en/content/articlepdf/2018/me/c8me00012c","abstract":"Traditional machine learning (ML) metrics overestimate model performance for materials discovery.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Machine Learning in Materials Science"],"details":null,"provenance":"openalex","attribution":"Abstract of \"Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery\" by Bryce Meredig, Erin Antono, Carena Church et al., https://doi.org/10.1039/c8me00012c, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified.","topics":[{"slug":"ai-for-science","name":"AI for Science","domain_slug":"ai"}],"cited_in_dossiers":[]}