<?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-llS3-1788874725-s840659201</doi_batch_id>
		<timestamp>1788874725</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>2026</year>
				</publication_date>
				<journal_volume>
					<volume>5</volume>
				</journal_volume>
				<issue>1</issue>
			</journal_issue>
			<journal_article publication_type="full_text">
				<titles>
					<title>Human Oversight as System Design: A Conceptual Reframing of Control in Semi-Autonomous Materials AI</title>
				</titles>
								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>Ahmed</given_name>
            <surname>El-Kholy</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Nour</given_name>
            <surname>Abdelrahman</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Karim</given_name>
            <surname>Hassan</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Mona</given_name>
            <surname>Saad</surname>
					</person_name>
          				</contributors>
								<publication_date>
					<year>2026</year>
				</publication_date>
				<doi_data>
					<doi>10.68159/s840659201</doi>
					<resource>https://iamrp.net/pub/journal/1/article/s840659201</resource>
				</doi_data>
				<citation_list>
          					<citation key="rk-10.68159/s840659201-86020164-c407-481c-b51f-5d461a13e26b">
					  <unstructured_citation>Xu P, Ji X, Li M, Lu W. Small data machine learning in materials science. npj Comput Mater. 2023;9(1):42.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/s840659201-e947e5a5-5de6-45f2-a53c-4d67e309154b">
					  <unstructured_citation>Morgan D, Jacobs R. Opportunities and challenges for machine learning in materials science. Annu Rev Mater Res. 2020;50:71-103.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/s840659201-7c264d5b-d925-4cc7-aac3-3c564be1691a">
					  <unstructured_citation>Fujii M. Significance of materials informatics and the development of new materials. JSAP Rev. 2022;2022:220416.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/s840659201-47918104-522b-41cb-85e2-ed70ecea71c9">
					  <unstructured_citation>Noack MM, Doerk GS, Li R, Streit JK, Vaia RA, Yager KG, et al. Autonomous materials discovery driven by gaussian process regression. Sci Rep. 2020;10:17663.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/s840659201-aaebb73c-61cb-4711-88ae-55e2d908cb9d">
					  <unstructured_citation>Li C, Zheng K. Methods, progresses, and opportunities of materials informatics. InfoMat. 2023;5(6):e12425.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/s840659201-1f18763e-88ee-49da-9865-d79f5b371c45">
					  <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/s840659201-3cf71e99-c620-4ef8-85af-d6a75bd5b5a7">
					  <unstructured_citation>Zhang H, Chen WW, Rondinelli JM, Chen W. ET-AL: Entropy-targeted active learning for bias mitigation in materials data. arXiv. 2022;2211.07881.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/s840659201-c1d7815b-93ec-4352-bcf6-f3654df9fb85">
					  <unstructured_citation>Kumagai M, Ando Y, Tanaka A, Tsuda K, Katsura Y, Kurosaki K. Effects of data bias on machine-learning–based material discovery using experimental property data. Adv Mater Interfaces. 2022;9:2109447.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/s840659201-3f67a24d-1222-43ea-9c8e-ad6341acffeb">
					  <unstructured_citation>Zhang H, Chen W, Rondinelli JM, Chen W. Mitigating bias in scientific data: A materials science case study. NeurIPS. 2023.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/s840659201-37983f2c-ae19-4161-b948-3dc4e8583844">
					  <unstructured_citation>Oviedo F, et al. Explainable machine learning in materials science. npj Comput Mater. 2022;8:184.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/s840659201-540c1f91-886a-4fff-9f00-02cd0789942d">
					  <unstructured_citation>Lu Z, Chen X, Liu X, Lin D, Wu Y, Zhang Y, et al. Interpretable machine-learning strategy for soft-magnetic property. npj Comput Mater. 2020;6:187.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/s840659201-24d2b2bf-0694-4c37-8c64-7577c7ac44ce">
					  <unstructured_citation>Wang HC, Schmidt J, Marques MA, Wirtz L, Romero AH. Symmetry-based computational search for novel binary and ternary 2d materials. 2D Mater. 2023;10(3):035007.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/s840659201-dc5ca623-afbb-4677-9279-c5e32ae73806">
					  <unstructured_citation>Chen D, Bai Y, Ament S, Zhao W, Guevarra D, Zhou L, et al. Automating crystal-structure phase mapping by combining deep learning with constraint reasoning. Nat Mach Intell. 2021;3(9):812-22.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/s840659201-904a5d98-0bea-4ba4-989b-c282d34e23c4">
					  <unstructured_citation>Koscher BA, et al. Autonomous, multiproperty-driven molecular discovery. Science. 2023;382(6672):eadl1407.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/s840659201-d5a1d568-9838-4d01-8320-7675b58e4f75">
					  <unstructured_citation>Hysmith H, Foadian E, Padhy SP, Kalinin SV, Moore RG, Ovchinnikova OS, et al. The future of self-driving laboratories: From human in the loop interactive ai to gamification. Digit Discov. 2024;3(4):456-68.</unstructured_citation>
						 <doi>10.1039/D4DD00040D</doi> 					</citation>
          					<citation key="rk-10.68159/s840659201-2aba5e9e-8c8c-4121-ae99-b782b2d7065f">
					  <unstructured_citation>Tobias AV, Wahab A. Autonomous ‘self-driving’ laboratories: A review of technology and policy implications. R Soc Open Sci. 2025;12(7):250646.</unstructured_citation>
						 <doi>10.1098/rsos.250646</doi> 					</citation>
          					<citation key="rk-10.68159/s840659201-b8387ee4-230f-4636-aecc-06ff664fb13e">
					  <unstructured_citation>Hung L, Yager JA, Monteverde D, Baiocchi D, Kwon H-K, Sun S, et al. Autonomous laboratories for accelerated materials discovery: A community survey and practical insights. Digit Discov. 2024;3:1273-9.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/s840659201-0446e50a-d19e-4806-8d42-11a87b08fe4e">
					  <unstructured_citation>Responsible ai in materials science contexts. Various sources. 2023-2024.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/s840659201-0f08e03d-931c-44bc-9b31-d9eea85c67f7">
					  <unstructured_citation>Ethical considerations in ai-driven scientific discovery. 2024.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/s840659201-d95bea7e-d2df-4ab1-99e4-4ac95323dc15">
					  <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/s840659201-5a395056-9f78-4d37-845c-f0827aec8b36">
					  <unstructured_citation>Pyzer-Knapp EO, Pitera JW, Staar PW, Takeda S, Laino T, Sanders DP, et al. Accelerating materials discovery using artificial intelligence, high performance computing and robotics. npj Comput Mater. 2022;8(1):84.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/s840659201-8c645cf4-7715-4cdc-8fc3-b5116d597f6b">
					  <unstructured_citation>Chelladurai U, Pandian S. A novel blockchain based electronic health record automation system for healthcare. J Ambient Intell Humaniz Comput. 2022;13(1):693-703.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/s840659201-592493b7-b585-4a41-9751-cea98acb69d1">
					  <unstructured_citation>Wang Z, Chen A, Tao K, Cai J, Han Y, Gao J, et al. Alphamat: A material informatics hub connecting data, features, models and applications. npj Comput Mater. 2023;9(1):130.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/s840659201-017ea1ae-04d9-4717-9c80-5d9b19bd67ce">
					  <unstructured_citation>Katsura Y, Akiyama M, Morito H, Fujioka M, Sugahara T. Systematic searches for new inorganic materials assisted by materials informatics. Sci Technol Adv Mater. 2024;26(1):2428154.</unstructured_citation>
						 <doi>10.1080/14686996.2024.2428154</doi> 					</citation>
          					<citation key="rk-10.68159/s840659201-3d741231-a9c3-4b00-abb1-15bc48038f95">
					  <unstructured_citation>Bayley O, Savino E, Slattery A, Noel T. Autonomous chemistry: Navigating self-driving labs in chemical and material sciences. Matter. 2024;7(7):2382-98.</unstructured_citation>
											</citation>
          				</citation_list>
			</journal_article>
		</journal>
	</body>
</doi_batch>
