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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>2022</year>
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					<title>Beyond Test-Set Error: A Conceptual Framework for Evaluating ML Potential Transferability Under Distribution Shift</title>
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            <given_name>Hiroshi</given_name>
            <surname>Tanaka</surname>
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            <given_name>Yuki</given_name>
            <surname>Sato</surname>
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            <given_name>Kenji</given_name>
            <surname>Mori</surname>
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            <given_name>Rina</given_name>
            <surname>Okabe</surname>
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          					<person_name sequence="additional" contributor_role="author">
            <given_name>Takashi</given_name>
            <surname>Ito</surname>
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					  <unstructured_citation>Deringer VL, Caro MA, Csányi G. Machine learning interatomic potentials as emerging tools for materials science. Adv Mater. 2019;31(46):1902765.</unstructured_citation>
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					  <unstructured_citation>Bartók AP, De S, Poelking C, Bernstein N, Kermode JR, Csányi G, et al. Machine learning unifies the modeling of materials and molecules. Sci Adv. 2017;3(12):e1701816.</unstructured_citation>
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					  <unstructured_citation>Zhang L, Han J, Wang H, Car R, E W. Deep potential molecular dynamics: A scalable model with the accuracy of quantum mechanics. Phys Rev Lett. 2018;120(14):143001.</unstructured_citation>
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					  <unstructured_citation>Behler J. Four generations of high-dimensional neural network potentials. Chem Rev. 2021;121(16):10037-72.</unstructured_citation>
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