{"id":"doi:10.1016/j.jechem.2022.11.047","kind":"article","title":"An integrated machine learning model for accurate and robust prediction of superconducting critical temperature","authors":["Jingzi Zhang","Ke Zhang","S. L. XU","Yi Li","Chengquan Zhong","Mengkun Zhao","Hua‐Jun Qiu","Mingyang Qin","X.‐D. Xiang","Kailong Hu","Xi Lin"],"author_count":11,"year":2022,"published":"2022-12-05","venue":"Journal of Energy Chemistry","cited_by":29,"doi":"10.1016/j.jechem.2022.11.047","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1016/j.jechem.2022.11.047","archive_url":"https://space-kitty.com/papers/doi:10.1016/j.jechem.2022.11.047","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W4310731422","pdf_url":null,"abstract":null,"abstract_license":null,"license":null,"is_open_access":false,"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":[]}