<?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-jSGJ-1788861347-e174870223</doi_batch_id>
		<timestamp>1788861347</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>2023</year>
				</publication_date>
				<journal_volume>
					<volume>2</volume>
				</journal_volume>
				<issue>1</issue>
			</journal_issue>
			<journal_article publication_type="full_text">
				<titles>
					<title>Conceptual Foundations for Value-Sensitive Design of Materials AI Systems</title>
				</titles>
								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>Claire</given_name>
            <surname>Dupont</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Julien</given_name>
            <surname>Martin</surname>
					</person_name>
          				</contributors>
								<publication_date>
					<year>2023</year>
				</publication_date>
				<doi_data>
					<doi>10.68159/e174870223</doi>
					<resource>https://iamrp.net/pub/journal/1/article/e174870223</resource>
				</doi_data>
				<citation_list>
          					<citation key="rk-10.68159/e174870223-758a4b5a-bfc0-42ae-acf2-cb7304fd0b5a">
					  <unstructured_citation>Islind AS, Willermark SM. Becoming a designer: The value of sensitive design situations for teaching and learning ethical design and design theory. Scand J Inf Syst. 2022;34(1):1.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e174870223-b483595c-a36b-488d-a3c0-5b1a4ae38837">
					  <unstructured_citation>Friedman B, Hendry DG. Value sensitive design: Shaping technology with moral imagination. Cambridge: MIT Press; 2019.</unstructured_citation>
						 <doi>10.7551/mitpress/7585.001.0001</doi> 					</citation>
          					<citation key="rk-10.68159/e174870223-f2975048-88f2-48cc-99a2-9a91f8be26a6">
					  <unstructured_citation>Van Der Hoven J, Manders-Huits N. Value-sensitive design. In: The ethics of information technologies. London: Routledge; 2020. p. 329-32.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e174870223-d88553b5-d0be-4610-885d-dd8b7490d832">
					  <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/e174870223-1e8cd85f-5e4d-4034-b00f-92e444285126">
					  <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/e174870223-cb5636f8-8f2c-41a5-a6df-37847c98ee4d">
					  <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/e174870223-fd261d5a-e394-4f21-8a53-5d1ef33c931f">
					  <unstructured_citation>Montoya JH, Aykol M, Anapolsky A, Gopal CB, Herring PK, Hummelshøj JS, et al. Toward autonomous materials research: Recent progress and future challenges. Appl Phys Rev. 2022;9(1).</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e174870223-33209e98-22ba-44ff-8b7f-e4258b4e9cf6">
					  <unstructured_citation>Umbrello S, Van de Poel I. Mapping value sensitive design onto AI for social good principles. AI Ethics. 2021;1(3):283-96.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e174870223-b037612c-01b5-44ef-b822-cee6be52d511">
					  <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(6):597-607.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e174870223-198947c9-46f6-452d-a00c-ace253204c6c">
					  <unstructured_citation>Melia HR, Muckley ES, Saal JE. Materials informatics and sustainability—the case for urgency. Data Cent Eng. 2021;2:e19.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e174870223-b46616fe-d759-4a36-9f5a-d1ab27e8fe91">
					  <unstructured_citation>Pilania G. Machine learning in materials science: From explainable predictions to autonomous design. Comput Mater Sci. 2021;193:110360.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e174870223-a52c8614-c80c-435b-9f66-2fcaafe91541">
					  <unstructured_citation>Hong Y, Hou B, Jiang H, Zhang J. Machine learning and artificial neural network accelerated computational discoveries in materials science. Wiley Interdiscip Rev Comput Mol Sci. 2020;10(3):e1450.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e174870223-5d75bb7c-c707-4a56-aed3-7c9806cc8371">
					  <unstructured_citation>López C. Artificial intelligence and advanced materials. Adv Mater. 2023;35(23):2208683.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e174870223-0e82efad-3e2f-4754-97fd-b8c824377038">
					  <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(3):033001.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e174870223-8e02b0cd-6ec3-4373-afdb-1982285a1ca0">
					  <unstructured_citation>Allen AE, Tkatchenko A. Machine learning of material properties: Predictive and interpretable multilinear models. Sci Adv. 2022;8(18):eabm7185.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e174870223-70fa6bbf-45e4-40f1-b1b5-1a20decb81f8">
					  <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/e174870223-c82ad208-28ec-4972-b210-209f3ba0e569">
					  <unstructured_citation>Umbrello S. Beneficial artificial intelligence coordination by means of a value sensitive design approach. Big Data Cogn Comput. 2019;3(1):5.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e174870223-05a65892-180c-4374-82cc-a5ec3f71ac41">
					  <unstructured_citation>Choudhary K, DeCost B, Chen C, Jain A, Tavazza F, Cohn R, et al. Recent advances and applications of deep learning methods in materials science. npj Comput Mater. 2022;8(1):59.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e174870223-fd2bccbc-9ce8-4ad4-915a-528ea4be91f6">
					  <unstructured_citation>Nasteski V. An overview of the supervised machine learning methods. Horizons. 2017;4:51-62.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e174870223-52b22299-ad02-4c53-bf0e-e5dda53b1e64">
					  <unstructured_citation>Piovesan D, Tan JB, Becker A, Banuelos J, Narasappa N, DiRenzo D, et al. Targeting CD73 with AB680 (Quemliclustat), a novel and potent small-molecule CD73 inhibitor, restores immune functionality and facilitates antitumor immunity. Mol Cancer Ther. 2022;21(6):948-59.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e174870223-6ea8a7b5-93bc-40d5-b311-a6a87e664bc6">
					  <unstructured_citation>Friedman B, Hendry DG, Borning A. A survey of value sensitive design methods. Found Trends Hum Comput Interact. 2017;11(2):63-125.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e174870223-55ccf77e-ea01-40d3-852a-ad508c0f1ff8">
					  <unstructured_citation>Umbrello S, De Bellis AF. A value-sensitive design approach to intelligent agents. In: Artificial intelligence safety and security. Boca Raton: CRC Press; 2018. p. 395-409.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e174870223-7db807db-2b20-4cd1-b9c3-9126956f639b">
					  <unstructured_citation>Stein HS, Gregoire JM. Progress and prospects for accelerating materials science with automated and autonomous workflows. Chem Sci. 2019;10(42):9640-9.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e174870223-7a161691-bede-49d4-bd23-e2ba396078ac">
					  <unstructured_citation>Suh C, Fare C, Warren JA, Pyzer-Knapp EO. Evolving the materials genome: How machine learning is fueling the next generation of materials discovery. Annu Rev Mater Res. 2020;50(1):1-25.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e174870223-8bab03db-fbc9-4661-8f6b-98ff4733c6c2">
					  <unstructured_citation>National Academies of Sciences, Engineering, and Medicine. Autonomous materials discovery and optimization: Proceedings of a workshop–in brief. Washington DC: National Academies Press; 2021.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e174870223-6d2250d8-f4fb-4e5a-bd2e-7f7b85357152">
					  <unstructured_citation>Ting JM, Tamayo-Mendoza T, Petersen SR, Van Reet J, Ahmed UA, Snell NJ, et al. Frontiers in nonviral delivery of small molecule and genetic drugs, driven by polymer chemistry and machine learning for materials informatics. Chem Commun. 2023;59(96):14197-209.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e174870223-04a3df79-fd1e-4de0-a4a6-4876765b3040">
					  <unstructured_citation>Brown DS, Schneider J, Dragan A, Niekum S. Value alignment verification. In: International Conference on Machine Learning. Brookline: PMLR; 2021. p. 1105-15.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e174870223-edbbdee5-7747-4c56-baf2-b9e945958a8e">
					  <unstructured_citation>Lv Z, Poiesi F, Dong Q, Lloret J, Song H. Deep learning for intelligent human–computer interaction. Appl Sci. 2022;12(22):11457.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e174870223-91f8eaeb-2846-46ef-bb7d-98fae3894a80">
					  <unstructured_citation>Boyd K. Designing up with value-sensitive design: Building a field guide for ethical ML development. In: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency. New York: ACM; 2022. p. 2069-82.</unstructured_citation>
											</citation>
          				</citation_list>
			</journal_article>
		</journal>
	</body>
</doi_batch>
