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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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					<year>2026</year>
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					<volume>5</volume>
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				<issue>1</issue>
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					<title>From Correlations to Design Rules: A Conceptual Model of Knowledge Extraction in Materials AI</title>
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          					<person_name sequence="first" contributor_role="author">
            <given_name>Claire</given_name>
            <surname>Dupont</surname>
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            <given_name>Julien</given_name>
            <surname>Martin</surname>
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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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          					<citation key="rk-10.68159/e653604936-e86c88da-38e9-429c-942b-71c789548ca1">
					  <unstructured_citation>Chen C, Ong SP. A universal graph deep learning interatomic potential for the periodic table. Nat Comput Sci. 2022;2(11):718-28.</unstructured_citation>
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					  <unstructured_citation>Foppa L, Sutton C, Ghiringhelli LM. Reproducibility in materials informatics: Lessons from ‘a general-purpose machine learning framework for predicting properties of inorganic materials’. Digit Discov. 2024;3(2):281-6.</unstructured_citation>
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          					<citation key="rk-10.68159/e653604936-baeeee44-e8cf-4b64-82ea-df7920fcad61">
					  <unstructured_citation>Gong S. The significance of materials informatics on material science. Appl Comput Eng. 2024;58:208-14.</unstructured_citation>
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          					<citation key="rk-10.68159/e653604936-50749c89-2c44-4216-8883-de6bc733ca84">
					  <unstructured_citation>Schwartz R, Vassilev A, Greene K, Perine L, Burt A, Hall P. Towards a standard for identifying and managing bias in artificial intelligence. NIST Spec Publ. 2022;1270:1-86.</unstructured_citation>
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          					<citation key="rk-10.68159/e653604936-7271d1f2-3fca-44a1-a304-c4331672db2d">
					  <unstructured_citation>Leavy S, O’Sullivan B, Siapera E. Data, power and bias in artificial intelligence. AI Soc Good Workshop. 2020:1-6.</unstructured_citation>
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          					<citation key="rk-10.68159/e653604936-59eb0fc1-8cd9-41e8-b887-760c35fa302e">
					  <unstructured_citation>Dunjic M. Values in science and ai alignment research. Soc Epistemol. 2024;38(4):458-75.</unstructured_citation>
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          					<citation key="rk-10.68159/e653604936-22ea60e0-d6c9-445f-9722-5c0ef6acb222">
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          					<citation key="rk-10.68159/e653604936-01312b62-1fc1-4098-8dbc-2e11ceafb7cd">
					  <unstructured_citation>Gomez-Bombarelli R, et al. Materials informatics: A review of ai and machine learning tools, platforms, data repositories, and applications to architectured porous materials. Mater Today Commun. 2024;40:110198.</unstructured_citation>
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          					<citation key="rk-10.68159/e653604936-e0c6fdaf-d3df-4423-8e7d-169f59b97999">
					  <unstructured_citation>Chaudhuri A, et al. Artificial intelligence in materials by design: Critical review and perspectives on materials informatics to generative and agentic intelligence. Mater Des. 2024;in press.</unstructured_citation>
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					  <unstructured_citation>Merchant A, et al. Data integrity in materials science in the era of ai: Balancing accelerated discovery with responsible science and innovation. J Mater Chem A. 2024;in press.</unstructured_citation>
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          					<citation key="rk-10.68159/e653604936-83edd9d8-cb03-4dda-b998-8af9b71e55ca">
					  <unstructured_citation>Chen A, McCloskey P, Sorger VJ. Bias in ai-based models for medical applications: Challenges and mitigation strategies. npj Digit Med. 2024;7(1):60.</unstructured_citation>
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          					<citation key="rk-10.68159/e653604936-340ca1a6-65d5-46d0-873b-f702edcbde0b">
					  <unstructured_citation>Asooja K, et al. Systematic literature review on bias mitigation in generative ai. AI Ethics. 2024;in press.</unstructured_citation>
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					  <unstructured_citation>Hamidieh K, et al. Researchers reduce bias in ai models while preserving or improving accuracy. arXiv. 2024;2406.16846.</unstructured_citation>
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					  <unstructured_citation>Wu X, et al. Addressing bias in generative ai: Challenges and research opportunities in information management. J Bus Res. 2024;172:114425.</unstructured_citation>
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					  <unstructured_citation>Leavy S. Gender bias in artificial intelligence: The need for diversity and gender theory in machine learning. In: Proceedings of the 1st international workshop on gender equality in software engineering; 2018:14-6.</unstructured_citation>
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          					<citation key="rk-10.68159/e653604936-c929e968-1b3a-4a8e-87a6-b24bdb3c18bd">
					  <unstructured_citation>Leavy S. Uncovering gender bias in media coverage of politicians with machine learning. arXiv preprint arXiv:2005.07734. 2020.</unstructured_citation>
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					  <unstructured_citation>Ashik Shahul Hameed M, et al. Bias mitigation via synthetic data generation: A review. Electronics. 2024;13(19):3909.</unstructured_citation>
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					  <unstructured_citation>Koskinen I. We still have no satisfactory social epistemology of ai-based science: A response to peters. Soc Epistemol Rev Reply Collect. 2024;13(5):11-7.</unstructured_citation>
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					  <unstructured_citation>Sikimic V. The use of AI and epistemic values in science. Eindhoven University of Technology; 2024.</unstructured_citation>
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					  <unstructured_citation>Stehr N. Social scientific knowledge about knowledge and information. Epistemol Philos Sci. 2023;60(3):131-70.</unstructured_citation>
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          					<citation key="rk-10.68159/e653604936-56acef4f-18ae-4a03-8fb2-4cf041dbaf71">
					  <unstructured_citation>Baeva L. Epistemic status of artificial intelligence in medical practice: Ethical challenges. J Digit Diagn. 2024;5(3):319-25.</unstructured_citation>
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					  <unstructured_citation>Flores L, Kim S, Young SD. Addressing bias in artificial intelligence for public health surveillance. J Med Ethics. 2024;50(3):190-4.</unstructured_citation>
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