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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>2024</year>
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					<volume>3</volume>
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				<issue>2</issue>
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					<title>A Conceptual Framework for Detecting Scientific Illusions in Generative Materials Outputs</title>
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          					<person_name sequence="first" contributor_role="author">
            <given_name>Alejandro</given_name>
            <surname>Torres</surname>
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            <given_name>Miguel</given_name>
            <surname>Fernandez</surname>
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					<year>2024</year>
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					  <unstructured_citation>Gomes CP, Selman B, Gregoire JM. Artificial intelligence for materials discovery. MRS Bull. 2019;44(7):538-44.</unstructured_citation>
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          					<citation key="rk-10.68159/f818549890-9e6ac8fb-ef77-4729-9738-c778cd89f474">
					  <unstructured_citation>Fuhr AS, Sumpter BG. Deep generative models for materials discovery and machine learning-accelerated innovation. Front Mater. 2022;9:865270.</unstructured_citation>
						 <doi>10.3389/fmats.2022.865270</doi> 					</citation>
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					  <unstructured_citation>Gomez-Villa A, Martín A, Vazquez-Corral J, Bertalmío M, Malo J. On the synthesis of visual illusions using deep generative models. J Vis. 2022;22(8):2.</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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					  <unstructured_citation>Schmidt J, Marques MR, Botti S, Marques MA. Recent advances and applications of machine learning in solid-state materials science. npj Comput Mater. 2019;5(1):83.</unstructured_citation>
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          					<citation key="rk-10.68159/f818549890-dbf64de9-0605-48af-88b7-8c73fca4dfec">
					  <unstructured_citation>Gómez-Bombarelli R, Wei JN, Duvenaud D, Hernández-Lobato JM, Sánchez-Lengeling B, Sheberla D, et al. Automatic chemical design using a data-driven continuous representation of molecules. ACS Cent Sci. 2018;4(2):268-76.</unstructured_citation>
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					  <unstructured_citation>Zunger A. Inverse design in search of materials with target functionalities. Nat Rev Chem. 2018;2(4):0121.</unstructured_citation>
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					  <unstructured_citation>Otyepka M, Pykal M, Otyepka M. Advancing materials discovery through artificial intelligence. Appl Mater Today. 2025;47:102981.</unstructured_citation>
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					  <unstructured_citation>Menon D, Ranganathan R. A generative approach to materials discovery, design, and optimization. ACS Omega. 2022;7(30):25958-73.</unstructured_citation>
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          					<citation key="rk-10.68159/f818549890-e214129b-b449-4ad3-9945-37b7b2136b47">
					  <unstructured_citation>Aboutalebi SH. Ensuring data integrity in AI-driven materials science: Why f-sum rules and Kramers-Kronig relations matter. Nanoscale Adv Mater. 2025;2(1):10-5.</unstructured_citation>
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					  <unstructured_citation>Takahara I, Mizoguchi T, Liu B. Accelerated inorganic materials design with generative AI agents. Cell Rep Phys Sci. 2025;6(12):103019.</unstructured_citation>
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					  <unstructured_citation>Kochkov D, Smith JA, Alieva A, Wang Q, Brenner MP, Hoyer S. Machine learning–accelerated computational fluid dynamics. Proc Natl Acad Sci U S A. 2021;118(21):e2101784118.</unstructured_citation>
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					  <unstructured_citation>Pan Y, Hou H, Pei X, Zhao Y. Feature purify: An examination of spurious correlations in high-entropy alloys. Mater Des. 2024;239:112785.</unstructured_citation>
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					  <unstructured_citation>Belkina M, Daniel S, Nikolic S, Haque R, Lyden S, Neal P, et al. Implementing generative AI (GenAI) in higher education: A systematic review of case studies. Comput Educ Artif Intell. 2025;8:100407.</unstructured_citation>
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					  <unstructured_citation>Sun Y, Sheng D, Zhou Z, Wu Y. AI hallucination: Towards a comprehensive classification of distorted information in artificial intelligence-generated content. Humanit Soc Sci Commun. 2024;11(1):1-4.</unstructured_citation>
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					  <unstructured_citation>Nam J, Mo S, Lee J, Shin J. Breaking the spurious causality of conditional generation via fairness intervention with corrective sampling. arXiv preprint arXiv:2212.02090. 2022 Dec 5.</unstructured_citation>
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					  <unstructured_citation>Ghiurău D, Popescu DE. Distinguishing reality from AI: Approaches for detecting synthetic content. Computers. 2024;14(1):1.</unstructured_citation>
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					  <unstructured_citation>Gray KL, Davis JP, Bunce C, Noyes E, Ritchie KL. Training human super-recognizers’ detection and discrimination of AI-generated faces. R Soc Open Sci. 2025;12(11):250921.</unstructured_citation>
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					  <unstructured_citation>Merchant A, Batzner S, Schoenholz SS, Aykol M, Cheon G, Cubuk ED. Scaling deep learning for materials discovery. Nature. 2023;624(7990):80-5.</unstructured_citation>
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					  <unstructured_citation>Sanchez-Lengeling B, Aspuru-Guzik A. Inverse molecular design using machine learning: Generative models for matter engineering. Science. 2018;361(6400):360-5.</unstructured_citation>
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					  <unstructured_citation>Isayev O, Oses C, Toher C, Gossett E, Curtarolo S, Tropsha A. Universal fragment descriptors for predicting properties of inorganic crystals. Nat Commun. 2017;8(1):15679.</unstructured_citation>
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