{"id":"doi:10.1515/ijmr-2020-7986","kind":"article","title":"Predicting doped Fe-based superconductor critical temperature from structural and topological parameters using machine learning","authors":["Yun Zhang","Xiaojie Xu"],"author_count":2,"year":2021,"published":"2021-01-01","venue":"International Journal of Materials Research (formerly Zeitschrift fuer Metallkunde)","cited_by":98,"doi":"10.1515/ijmr-2020-7986","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1515/ijmr-2020-7986","archive_url":"https://space-kitty.com/papers/doi:10.1515/ijmr-2020-7986","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W3130656729","pdf_url":"https://www.degruyter.com/document/doi/10.1515/ijmr-2020-7986/pdf","abstract":null,"abstract_license":null,"license":null,"is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Machine Learning in Materials Science"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0).","topics":[{"slug":"superconductor-discovery","name":"Computational & AI-Driven Superconductor Discovery","domain_slug":"superconductivity"}],"cited_in_dossiers":[]}