{"id":"doi:10.1021/acs.chemrev.1c00347","kind":"review","title":"Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning","authors":["Aditya Nandy","Chenru Duan","Michael G. Taylor","Fang Liu","Adam H. Steeves","Heather J. Kulik"],"author_count":6,"year":2021,"published":"2021-07-14","venue":"Chemical Reviews","cited_by":357,"doi":"10.1021/acs.chemrev.1c00347","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1021/acs.chemrev.1c00347","archive_url":"https://space-kitty.com/papers/doi:10.1021/acs.chemrev.1c00347","updated":null,"pmid":"34260198","pmcid":null,"openalex_id":"W3181257149","pdf_url":null,"abstract":null,"abstract_license":null,"license":"cc-by-nc-nd","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":"ai-for-science","name":"AI for Science","domain_slug":"ai"}],"cited_in_dossiers":[]}