<?xml version="1.0" encoding="UTF-8"?><doi_batch version="4.3.7" xmlns="http://www.crossref.org/schema/4.3.7" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.crossref.org/schema/4.3.7 http://www.crossref.org/schema/deposit/crossref4.3.7.xsd">
		<head>
		<doi_batch_id>iamrp.net-I2w8-1791561621-a640642567</doi_batch_id>
		<timestamp>1791561621</timestamp>
		<depositor>
			<depositor_name>Institute for Advanced Materials Research Press</depositor_name>
			<email_address>info@iamrp.net</email_address>
		</depositor>
		<registrant>Institute for Advanced Materials Research Press</registrant>
	</head>
	<body>
		<journal>
			<journal_metadata>
				<full_title>Journal of Artificial Intelligence for Materials Science</full_title>
				<abbrev_title>J. Artif. Intell. Mater. Sci.</abbrev_title>
				<issn>3149-8957</issn>
			</journal_metadata>
			<journal_issue>
				<publication_date>
					<year>2025</year>
				</publication_date>
				<journal_volume>
					<volume>4</volume>
				</journal_volume>
				<issue>2</issue>
			</journal_issue>
			<journal_article publication_type="full_text">
				<titles>
					<title>The Problem of Normative Assumptions in Materials AI Objective Functions</title>
				</titles>
								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>Ricardo</given_name>
            <surname>Alves</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Bruno</given_name>
            <surname>Costa</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Helena</given_name>
            <surname>Martins</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Nuno</given_name>
            <surname>Faria</surname>
					</person_name>
          				</contributors>
								<publication_date>
					<year>2025</year>
				</publication_date>
				<doi_data>
					<doi>10.68159/a640642567</doi>
					<resource>https://iamrp.net/pub/journal/1/article/a640642567</resource>
				</doi_data>
				<citation_list>
          					<citation key="rk-10.68159/a640642567-50c93cdc-1d8a-4f9f-b5eb-8512a4ef04ee">
					  <unstructured_citation>Chakraborty S. The fact/value dichotomy: Revisiting putnam and habermas. Philosophia. 2019;47(2):369-86.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/a640642567-ff573a77-274b-4162-a5f7-1e19ae99e1f0">
					  <unstructured_citation>Russell S. Human-compatible artificial intelligence. Hum Like Mach Intell. 2022;1:3-22.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/a640642567-390d63f4-735b-4952-a1a8-f966b3ef2c56">
					  <unstructured_citation>Mühlhoff R, Ruschemeier H. Updating purpose limitation for AI: A normative approach from law and philosophy. Int J Law Inf Technol. 2025;33:eaaf003.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/a640642567-579382ac-20f3-43c8-bfb4-70a0cf22d6f9">
					  <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>
											</citation>
          					<citation key="rk-10.68159/a640642567-b330df50-8701-4196-b306-80ad423f6a3c">
					  <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>
											</citation>
          					<citation key="rk-10.68159/a640642567-d1806981-0c1d-45fd-b57e-6476964599fc">
					  <unstructured_citation>Zunger A. Inverse design in search of materials with target functionalities. Nat Rev Chem. 2018;2(4):0121.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/a640642567-ab6a7994-c575-4710-9abc-155dcf6051ce">
					  <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>
											</citation>
          					<citation key="rk-10.68159/a640642567-7ab21d66-cdb7-4d10-99c4-4943626a04fe">
					  <unstructured_citation>Cercone N, McCalla G. Artificial intelligence: Underlying assumptions and basic objectives. J Am Soc Inf Sci. 1984;35(5):280-90.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/a640642567-76abe163-1e71-49da-8787-07d62fcb506d">
					  <unstructured_citation>Kaufman BE. The social welfare objectives and ethical principles of. In: The ethics of human resources and industrial relations. Champaign: LERA; 2005. p. 23-59.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/a640642567-81c52331-1a85-4625-b62b-253250ef1a26">
					  <unstructured_citation>Giannini F, Diligenti M, Maggini M, Gori M, Marra G. T-norms driven loss functions for machine learning. Appl Intell. 2023;53(15):18775-89.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/a640642567-a7b508c7-9320-45a9-b78d-6ee10490600c">
					  <unstructured_citation>De Gregorio G. The normative power of artificial intelligence. Ind J Global Legal Stud. 2023;30:55.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/a640642567-3cbce7d6-9115-4259-b0d2-b6a5f4f432ca">
					  <unstructured_citation>Trehern W, Ortiz-Ayala R, Atli KC, Arroyave R, Karaman I. Data-driven shape memory alloy discovery using artificial intelligence materials selection (AIMS) framework. Acta Mater. 2022;228:117751.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/a640642567-8f08ed09-2637-4a27-8bdc-f12860089343">
					  <unstructured_citation>Madika B, Saha A, Kang C, Buyantogtokh B, Agar J, Wolverton C, et al. Artificial intelligence for materials discovery, development, and optimization. ACS Nano. 2025;19(30):27116-58.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/a640642567-ae45586c-df9f-42f8-9f0b-b811a1159caa">
					  <unstructured_citation>Sadrossadat E, Basarir H, Karrech A, Elchalakani M. Multi-objective mixture design and optimisation of steel fiber reinforced UHPC using machine learning algorithms and metaheuristics. Eng Comput. 2022;38(Suppl 3):2569-82.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/a640642567-a9a5d8db-15c1-40c1-a8d6-39941a00cb4a">
					  <unstructured_citation>Prasittisopin L. Artificial intelligence-driven materiality: A systematic review and perspective. Eng Sci. 2025;35:1587.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/a640642567-51754e22-2d27-4044-8307-7751e9a682af">
					  <unstructured_citation>Kumar S, Choudhury S. Normative ethics, human rights, and artificial intelligence. AI Ethics. 2023;3(2):441-50.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/a640642567-6258a2d3-8f9a-4532-9f40-18bef5afb0dc">
					  <unstructured_citation>Wang J, Wang Y, Chen Y. Inverse design of materials by machine learning. Materials. 2022;15(5):1811.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/a640642567-aed48229-dac1-4031-9779-c98bb5542e34">
					  <unstructured_citation>Gabriel I. Artificial intelligence, values, and alignment. Minds Mach. 2020;30(3):411-37.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/a640642567-015b43e9-3ec4-4263-aff8-a5cf51716bcb">
					  <unstructured_citation>Reiss J. Fact-value entanglement in positive economics. J Econ Methodol. 2017;24(2):134-49.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/a640642567-781e234f-6983-4f4d-ac58-5e5d636cdd0e">
					  <unstructured_citation>Galos JL, Kivy MB, Grunenfelder L, Nomura KI, Saal JE. A critical review of approaches to teaching artificial intelligence in undergraduate materials engineering. In: 2025 ASEE Annual Conference Exposition; 2025.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/a640642567-8d60f42b-59f4-410c-a392-e89b7e8f7346">
					  <unstructured_citation>Çullhaj F. Complications (complexity) between normative and descriptive: A challenge for clarity. Balk J Philos. 2022;14(1):65-72.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/a640642567-6e5fcb70-debb-4413-b367-962025a48b74">
					  <unstructured_citation>Li JP, Polovina N, Konur S. A review of AI-driven engineering modelling and optimization: Methodologies, applications and future directions. Algorithms. 2026;19(2).</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/a640642567-e0b0982b-ebb1-403a-bd1f-9e0e39eb17a0">
					  <unstructured_citation>Raap M, Preuß M, Meyer-Nieberg S. Moving target search optimization–a literature review. Comput Oper Res. 2019;105:132-40.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/a640642567-5d84651d-9aaf-4bb7-bc1b-64720056f09b">
					  <unstructured_citation>Ramprasad R, Batra R, Pilania G, Mannodi-Kanakkithodi A, Kim C. Machine learning in materials informatics: Recent applications and prospects. npj Comput Mater. 2017;3(1):54.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/a640642567-3dfd60e0-8e4d-4f37-ab1b-80cf71e2d95f">
					  <unstructured_citation>Bagheri M, Bagheritabar M, Alizadeh S, Parizi MS, Matoufinia P, Luo Y. Machine-learning-powered information systems: A systematic literature review for developing multi-objective healthcare management. Appl Sci. 2024;15(1):296.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/a640642567-022315b7-002c-4762-b447-644c6ff74a8f">
					  <unstructured_citation>Niiniluoto I. Assessing value-laden technology. Rev Guillermo Ockham. 2024;22(2):5-17.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/a640642567-70ef2919-e18d-41ea-b02d-a0402d6acb79">
					  <unstructured_citation>Cheetham AK, Seshadri R. Artificial intelligence driving materials discovery? Perspective on the article: Scaling deep learning for materials discovery. Chem Mater. 2024;36(8):3490-5.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/a640642567-1839ffa1-9d61-40cb-ac7e-c9fad7480caf">
					  <unstructured_citation>Johnson JP. The fact-value question in early modern value theory. In: Value theory in philosophy and social science (RLE Social Theory). Routledge; 2014. p. 64-71.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/a640642567-b43af0ee-c90a-4b7a-b789-3256a9f99f82">
					  <unstructured_citation>Billard TJ. Theory and/as normative assumptions in political communication research. Political Communication Report. 30. Available from: https://nbn-resolving.org/urn:nbn:de:0168-ssoar-98561-6</unstructured_citation>
											</citation>
          				</citation_list>
			</journal_article>
		</journal>
	</body>
</doi_batch>
