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			<depositor_name>Institute for Advanced Materials Research Press</depositor_name>
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				<full_title>Journal of Artificial Intelligence for Materials Science</full_title>
				<abbrev_title>J. Artif. Intell. Mater. Sci.</abbrev_title>
				<issn>3149-8957</issn>
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				<publication_date>
					<year>2025</year>
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					<volume>4</volume>
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				<issue>1</issue>
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					<title>Governance and Responsible Use of Materials AI — Standards, Transparency, and Risk Management</title>
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            <given_name>Fatima Zahra</given_name>
            <surname>Amrani</surname>
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            <given_name>Youssef</given_name>
            <surname>Benali</surname>
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          					<person_name sequence="additional" contributor_role="author">
            <given_name>Samira</given_name>
            <surname>El-Haddad</surname>
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          					<person_name sequence="additional" contributor_role="author">
            <given_name>Karim</given_name>
            <surname>Idrissi</surname>
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					<year>2025</year>
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					  <unstructured_citation>DeCost BL, Hattrick-Simpers JR, Trautt Z, Kusne AG, Campo E, Green ML. Scientific AI in materials science: A path to a sustainable and scalable paradigm. Mach Learn Sci Technol. 2020;1(1):013001.</unstructured_citation>
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					  <unstructured_citation>Batra R, Song L, Ramprasad R. Emerging materials intelligence ecosystems propelled by machine learning. Nat Rev Mater. 2021;6(8):655-78.</unstructured_citation>
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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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          					<citation key="rk-10.68159/h097101046-84028332-fb89-4738-a3cd-de2d1f53c881">
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          					<citation key="rk-10.68159/h097101046-46e51278-ea2b-48e4-84b2-97fb625205b2">
					  <unstructured_citation>Oviedo F, Ferres JL, Buonassisi T, Butler KT. Interpretable and explainable machine learning for materials science and chemistry. Acc Mater Res. 2022;3(5):597-607.</unstructured_citation>
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          					<citation key="rk-10.68159/h097101046-0e7a0617-2e99-4e08-b72f-f0dcbfd355b9">
					  <unstructured_citation>Himanen L, Geilhufe M, Clark RM. Materials data science and how FAIR data management can enable it. Adv Theory Simul. 2021;4(5):2000243.</unstructured_citation>
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          					<citation key="rk-10.68159/h097101046-1832efa5-f292-42c8-b4bc-ff6b35b3165b">
					  <unstructured_citation>Himanen L, Geilhufe M, Clark RM. Data-driven materials science: status, challenges, and perspectives. Adv Sci. 2019;6(21):1900808.</unstructured_citation>
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          					<citation key="rk-10.68159/h097101046-ca35beb4-10f5-4825-8629-a778ead8abd4">
					  <unstructured_citation>Ramprasad R, Batra R, Pilania G, Mannodi-Kanakkithodi A, Kim C. Machine learning in materials science. npj Comput Mater. 2017;3(1):1-13.</unstructured_citation>
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          					<citation key="rk-10.68159/h097101046-883e3aa5-6af8-489a-8b6d-7842049484ab">
					  <unstructured_citation>Jain A, Ong SP, Hautier G, Chen W, Richards WD, Dacek S, et al. Commentary: The Materials Project: A materials genome approach to accelerating materials innovation. APL Mater. 2013;1(1):011002.</unstructured_citation>
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          					<citation key="rk-10.68159/h097101046-0b698b87-efbe-4260-9c69-d7da0f023b07">
					  <unstructured_citation>Wang AY, Murdock R, Kauwe S, Oliynyk AO, Gurlo A, Brgoch J, et al. Machine learning for materials scientists: An introductory guide toward best practices. Chem Mater. 2020;32(12):4954-65.</unstructured_citation>
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          					<citation key="rk-10.68159/h097101046-253f1093-5cd7-4ddc-b6c8-314453e5d312">
					  <unstructured_citation>Dan Y, Zhao Y, Li X, Li S, Hu M, Hu J. Generative deep learning for inorganic materials discovery. Matter. 2020;3(4):1074-92.</unstructured_citation>
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          					<citation key="rk-10.68159/h097101046-59cc4309-6e13-4dc6-96dd-7f0272ee56e4">
					  <unstructured_citation>Xie T, Grossman JC. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys Rev Lett. 2018;120(14):145301.</unstructured_citation>
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          					<citation key="rk-10.68159/h097101046-f21b5f32-0b8e-4b83-878f-509322e275ee">
					  <unstructured_citation>Chen H, Engkvist O, Wang Y, Olivecrona M, Blaschke T. The rise of deep learning in drug discovery. Drug Discov Today. 2018;23(6):1241-50.</unstructured_citation>
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          					<citation key="rk-10.68159/h097101046-55594574-280f-4f94-82d5-48d26f5759bc">
					  <unstructured_citation>MacLeod BP, Parlane FGL, Morrissey TD, Häse F, Roch LM, Dettelbach KE, et al. Self-driving laboratory for accelerated discovery of thin-film materials. Sci Adv. 2020;6(20):eaaz8867.</unstructured_citation>
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					  <unstructured_citation>Burger B, Maffettone PM, Gusev VV, Aitchison CM, Bai Y, Wang X, et al. A mobile robotic chemist. Nature. 2020;583(7815):237-41.</unstructured_citation>
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					  <unstructured_citation>Unke OT, Chmiela S, Gastegger M, Schütt KT, Sauceda HE, Müller KR. SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects. Nat Commun. 2021;12(1):7273.</unstructured_citation>
						 <doi>10.1038/s41467-021-27504-0</doi> 					</citation>
          					<citation key="rk-10.68159/h097101046-aec372a8-805c-4e02-8a60-1cd2f94ed122">
					  <unstructured_citation>Wilary DM, Butler KT. Interpretable machine learning models for predicting the electronic structure of high-entropy alloys. Phys Rev Mater. 2021;5(2):025002.</unstructured_citation>
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          					<citation key="rk-10.68159/h097101046-c8dcbfb5-a27e-4c9b-a730-fd6549c353ed">
					  <unstructured_citation>OECD. OECD Principles on Artificial Intelligence. 2019.</unstructured_citation>
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          					<citation key="rk-10.68159/h097101046-e094ef3f-baa0-4905-b132-cc56f21123e5">
					  <unstructured_citation>Owen R, Macnaghten P, Stilgoe J. Responsible research and innovation: From science in society to science for society, with society. Sci Public Policy. 2012;39(6):751-60.</unstructured_citation>
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					  <unstructured_citation>Dunn A, Wang Q, Jain A. Matbench 1.0: An updated materials benchmark for machine learning. Comput Mater Sci. 2020;184:109849.</unstructured_citation>
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					  <unstructured_citation>ISO/IEC. ISO/IEC TR 24028:2020 Information technology — Artificial intelligence — Overview of trustworthiness in artificial intelligence. 2020.</unstructured_citation>
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					  <unstructured_citation>Jha D, Ward L, Paul A, Liao W, Choudhary A, Chris Wolverton C, et al. ElemNet: Deep learning the chemistry of materials from only elemental composition. Sci Rep. 2018;8(1):17593.</unstructured_citation>
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					  <unstructured_citation>Karniadakis GE, Kevrekidis IG, Lu L, et al. Physics-informed machine learning. Nat Rev Phys. 2021;3(6):422-40.</unstructured_citation>
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					  <unstructured_citation>Stilgoe J, Owen R, Macnaghten P. Developing a framework for responsible innovation. Res Policy. 2013;42(9):1568-80.</unstructured_citation>
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