<?xml version="1.0" encoding="UTF-8"?><rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcq="http://purl.org/dc/terms/"><records count="1" morepages="false" start="1" end="1"><record rownumber="1"><dc:product_type>Journal Article</dc:product_type><dc:title>&lt;i&gt;Colloquium&lt;/i&gt; : Machine learning in nuclear physics</dc:title><dc:creator>Boehnlein, Amber; Diefenthaler, Markus; Sato, Nobuo; Schram, Malachi; Ziegler, Veronique; Fanelli, Cristiano; Hjorth-Jensen, Morten; Horn, Tanja; Kuchera, Michelle P.; Lee, Dean; Nazarewicz, Witold; Ostroumov, Peter; Orginos, Kostas; Poon, Alan; Wang, Xin-Nian; Scheinker, Alexander; Smith, Michael S.; Pang, Long-Gang</dc:creator><dc:corporate_author/><dc:editor/><dc:description/><dc:publisher/><dc:date>2022-09-01</dc:date><dc:nsf_par_id>10378772</dc:nsf_par_id><dc:journal_name>Reviews of Modern Physics</dc:journal_name><dc:journal_volume>94</dc:journal_volume><dc:journal_issue>3</dc:journal_issue><dc:page_range_or_elocation/><dc:issn>0034-6861</dc:issn><dc:isbn/><dc:doi>https://doi.org/10.1103/RevModPhys.94.031003</dc:doi><dcq:identifierAwardId>1713901; 2013047; 2012865; 2004601; 2012430; 2209442</dcq:identifierAwardId><dc:subject/><dc:version_number/><dc:location/><dc:rights/><dc:institution/><dc:sponsoring_org>National Science Foundation</dc:sponsoring_org></record></records></rdf:RDF>