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			<depositor_name>Institute for Advanced Materials Research Press</depositor_name>
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				<full_title>Journal of Computational and Data-Driven Materials Engineering</full_title>
				<abbrev_title>J. Comput. Data-Driven Mater. Eng.</abbrev_title>
				<issn>3149-9368</issn>
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					<year>2024</year>
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					<volume>3</volume>
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				<issue>1</issue>
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					<title>Discovery Recommendation Systems: Reframing Materials Selection Algorithms</title>
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          					<person_name sequence="first" contributor_role="author">
            <given_name>Wei</given_name>
            <surname>Liu</surname>
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            <given_name>Zhang</given_name>
            <surname>Min</surname>
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								<publication_date>
					<year>2024</year>
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					  <unstructured_citation>Butler KT, Davies DW, Cartwright H, Isayev O, Walsh A. Machine learning for molecular and materials science. Nature. 2018;559(7715):547-55.</unstructured_citation>
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