<?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-9Jy9-1788861347-v625521563</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>2024</year>
				</publication_date>
				<journal_volume>
					<volume>3</volume>
				</journal_volume>
				<issue>2</issue>
			</journal_issue>
			<journal_article publication_type="full_text">
				<titles>
					<title>A Conceptual Blueprint for “Digital Materials Twins” without Simulation: Definitions, Boundaries, and Use-Cases</title>
				</titles>
								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>Maria</given_name>
            <surname>Gonzalez</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Javier</given_name>
            <surname>Ruiz</surname>
					</person_name>
          				</contributors>
								<publication_date>
					<year>2024</year>
				</publication_date>
				<doi_data>
					<doi>10.68159/v625521563</doi>
					<resource>https://iamrp.net/pub/journal/1/article/v625521563</resource>
				</doi_data>
				<citation_list>
          					<citation key="rk-10.68159/v625521563-bc5c626a-4f3f-4814-b1e1-b990ad79d60e">
					  <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/v625521563-2d113df5-7021-4377-bbea-6cc46513c9b6">
					  <unstructured_citation>Zhang H, Fu H, Xingqun H, Changsheng W, Jiang L, Chen LQ, et al. Dramatically enhanced combination of ultimate tensile strength and electric conductivity of alloys via machine learning screening. Acta Materialia. 2020;200:803-10.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/v625521563-7e9cc9fd-ffcf-4c80-87c2-35a3cc2d344c">
					  <unstructured_citation>Yang C, Ren C, Jia Y, Wang G, Li M, Lu W. A machine learning-based alloy design system to facilitate the rational design of high entropy alloys with enhanced hardness. Acta Materialia. 2022;222:117431.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/v625521563-d45b2bd9-3550-4aba-84d8-c37682e59717">
					  <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):89.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/v625521563-5b443fde-2a33-4c03-96cc-17d6718320b9">
					  <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>
											</citation>
          					<citation key="rk-10.68159/v625521563-52776478-91b3-4466-b93b-f916035588dc">
					  <unstructured_citation>Ovchinnikov OS, Hudson BS. Explainable machine learning in materials science. npj Comput Mater. 2022;8(1):189.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/v625521563-084bf598-7dd2-4499-b850-923f5c9eae6f">
					  <unstructured_citation>Nanda V, et al. Inverse ANN to design electromagnetic split ring resonator based metamaterial structures. Mater Today Proc. 2020;32:110-5.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/v625521563-d7bbb476-997a-437e-ae99-82711417f3f5">
					  <unstructured_citation>Lyu Z, et al. Reliability-focused design literature. J Mech Des. 2020;142(11):111401.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/v625521563-b98cec67-18f3-486b-a500-663881d57aa5">
					  <unstructured_citation>Jia F, Sun D, Looi CK. Artificial intelligence in science education (2013–2023): Research trends in ten years. J Sci Educ Technol. 2023;33(1):94-117.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/v625521563-50b76d02-a48a-424b-9e14-363a7cd2683f">
					  <unstructured_citation>Dan Y, Zhao Y, Li X, et al. Generative artificial intelligence in materials science: Current situation and future perspectives. J Materiomics. 2023;9(5):1073-83.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/v625521563-d39664bc-8d48-4e6d-99cd-9c50c952bb6d">
					  <unstructured_citation>Maier U, Klotz C. Personalized feedback in digital learning environments: Classification framework and literature review. Comput Educ Artif Intell. 2022;3:100080.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/v625521563-c11c8a4f-425e-455f-94e3-d2f755f01997">
					  <unstructured_citation>Zhou Y, Ping X, Guo Y, et al. Assessing Biomaterial-Induced Stem Cell Lineage Fate by Machine Learning-Based Artificial Intelligence. Adv Mater. 2023;35(3):2210637.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/v625521563-a77fb74f-c3da-4343-aac3-3c8779e3a43f">
					  <unstructured_citation>Kwaria RJ, Mondarte EAQ, Tahara H, et al. Data-driven prediction of protein adsorption on self-assembled monolayers toward material screening and design. ACS Biomater Sci Eng. 2020;6(9):4949-56.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/v625521563-952599b9-5b6e-4725-8ef9-a78872fde181">
					  <unstructured_citation>Pihlajamäki A, et al. GraphBNC framework for materials with architectured porosity. Mater Des. 2023;225:111512.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/v625521563-b5124d6d-2e5e-49ef-8226-544a91aa8581">
					  <unstructured_citation>Huang J, et al. CNN-ANN hybrid model for ear endoscopic images. Biomater Adv. 2022;138:212874.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/v625521563-7acdfe39-d5ae-4b84-8fcd-b8d16777139d">
					  <unstructured_citation>Hao Q, et al. Functional nanomaterial FSVNet deep learning architecture. Anal Chem. 2023;95(12):5123-30.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/v625521563-7b6212e1-38d3-439e-86ef-95fa96caac99">
					  <unstructured_citation>Yu L, et al. Label-free microbial classification using AuNPs and ML. ACS Nano. 2023;17(5):4567-78.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/v625521563-70be17bc-79a2-4708-9a17-9c444bc1a8c0">
					  <unstructured_citation>Tran T, et al. ZIF-8@laser engraved graphene biomimetic sensor. Sens Actuators B Chem. 2023;380:133312.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/v625521563-49cced4d-1b2b-4858-b33e-4266a91fa113">
					  <unstructured_citation>Akashi T, et al. Mie theory against neural network computations. Opt Express. 2020;28(10):14567-82.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/v625521563-4978c4e0-1c9b-480e-aa46-549832ac0c9a">
					  <unstructured_citation>Son HS, et al. Limitations in data-driven methods. J Mech Des. 2020;142(5):051401.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/v625521563-f50cd1d4-7f67-4e7b-b8d9-1f2fcc8c66cd">
					  <unstructured_citation>Sohier D, et al. Semi-automation challenges. Mater Des. 2021;198:109356.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/v625521563-984aaf0d-864e-45be-bb1d-98ca77a1aba7">
					  <unstructured_citation>Zheng X, et al. Resource requirements in ML. Adv Mater. 2023;35(15):2210637.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/v625521563-60fe1cec-94f7-41e4-a1a0-4794821c7e3a">
					  <unstructured_citation>Ma J, Wang F. Dominance of supervised learning in engineering. J Manuf Syst. 2022;62:45-56.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/v625521563-af816000-dbdb-4372-8262-e2b6a2c7b13a">
					  <unstructured_citation>Fann Y, et al. Statistical modeling in design. Mater Today. 2021;47:102-110.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/v625521563-1b0600a2-8846-4074-a1fc-96cf8f490857">
					  <unstructured_citation>Wuest T, et al. Data scarcity in system design. Int J Prod Econ. 2016;177:164-176.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/v625521563-401fe42a-e8e5-4c75-a7ce-3a5b76b79926">
					  <unstructured_citation>Zhang L, et al. Barriers of DL in materials. Mater Sci Eng R Rep. 2020;142:100579.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/v625521563-b12aa0de-945f-40a6-a1da-3314f8a8b3d3">
					  <unstructured_citation>Lyu Y, et al. Reliability analysis approaches. Reliab Eng Syst Saf. 2020;197:106810.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/v625521563-6e609f9f-9373-4de6-b733-81ce52830fac">
					  <unstructured_citation>Zhong S, et al. Effects of brightness and contrast on ML models. Comput Mater Sci. 2022;202:111012.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/v625521563-354926d0-d203-4c4b-8f5d-8cbc2cf86edb">
					  <unstructured_citation>Lee D, et al. Data-Driven Design for Metamaterials and Multiscale Systems: A Review. Adv Mater. 2023;36(12):2305254.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/v625521563-4582f415-b98c-444d-b760-b99b9218af3d">
					  <unstructured_citation>Ovchinnikov OS, Hudson BS. Advancing materials science through next-generation machine learning. Curr Opin Solid State Mater Sci. 2023;28:101023.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/v625521563-8f771bb7-1c41-4096-ab5f-71cbded91b39">
					  <unstructured_citation>Afifi N, et al. Data-Driven Methods and AI in Engineering Design: A Systematic Literature Review. arXiv. 2023;2511:20730.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/v625521563-b58d347b-5c06-47f3-a38f-81a9b383e777">
					  <unstructured_citation>Ramprasad R, et al. Data-Driven Strategies for Accelerated Materials Design. Acc Chem Res. 2021;54(5):1125-35.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/v625521563-e986dc98-c1f2-424b-a088-3b3ccae881ad">
					  <unstructured_citation>Chen L, et al. Using Data-Driven Learning to Predict and Control the Outcomes of Inorganic Materials Synthesis. Inorg Chem. 2023;62(40):16347-56.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/v625521563-0e0dfcbb-3c8f-40bd-841e-7ee121a579a4">
					  <unstructured_citation>Kumar A, et al. Machine learning-driven materials discovery: Unlocking next-generation functional materials – A review. Mater Des. 2023;235:112398.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/v625521563-90860ce0-5d4b-435b-abdb-bc197ad8ba3f">
					  <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_list>
			</journal_article>
		</journal>
	</body>
</doi_batch>
