{"id":"doi:10.1016/j.commatsci.2019.109203","kind":"article","title":"Evaluating explorative prediction power of machine learning algorithms for materials discovery using k -fold forward cross-validation","authors":["Zheng Xiong","Yuxin Cui","Zhonghao Liu","Yong Zhao","Ming Hu","Jianjun Hu"],"author_count":6,"year":2019,"published":"2019-08-27","venue":"Computational Materials Science","cited_by":337,"doi":"10.1016/j.commatsci.2019.109203","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1016/j.commatsci.2019.109203","archive_url":"https://space-kitty.com/papers/doi:10.1016/j.commatsci.2019.109203","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W2970591347","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"},{"slug":"ai-for-science","name":"AI for Science","domain_slug":"ai"}],"cited_in_dossiers":[]}