<?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-9fwg-1793433665-y079843774</doi_batch_id>
		<timestamp>1793433665</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>Temporal Myopia in AI-Accelerated Materials Research</title>
				</titles>
								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>Lucas</given_name>
            <surname>Pereira</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Bruno</given_name>
            <surname>Martins</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Renata</given_name>
            <surname>Azevedo</surname>
					</person_name>
          				</contributors>
								<publication_date>
					<year>2023</year>
				</publication_date>
				<doi_data>
					<doi>10.68159/y079843774</doi>
					<resource>https://iamrp.net/pub/journal/1/article/y079843774</resource>
				</doi_data>
				<citation_list>
          					<citation key="rk-10.68159/y079843774-1b66b68d-33aa-4911-9edf-39af42b7bb13">
					  <unstructured_citation>Zhou H, Xu J, Qin X, Zhang J, Zou W, Shakouri M, et al. Machine learning-driven material intelligence research and development. Nano Res. 2025;18(12):94908095.</unstructured_citation>
						 <doi>10.26599/NR.2025.94908095</doi> 					</citation>
          					<citation key="rk-10.68159/y079843774-d7e52910-8bce-4e88-84f5-f5edd0783610">
					  <unstructured_citation>Otyepka M, Pykal M, Otyepka M. Advancing materials discovery through artificial intelligence. Appl Mater Today. 2025;47:102981.</unstructured_citation>
						 <doi>10.1016/j.apmt.2025.102981</doi> 					</citation>
          					<citation key="rk-10.68159/y079843774-dbc95d5f-bc27-43c8-a884-854b7d2111a5">
					  <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>
						 <doi>10.1021/acs.chemmater.4c00643</doi> 					</citation>
          					<citation key="rk-10.68159/y079843774-3ae3fa6c-e27e-40d8-8283-ada7d107ddef">
					  <unstructured_citation>Jiang X, Xue D, Bai Y, Wang WY, Liu J, Yang M, et al. AI4Materials: Transforming the landscape of materials science and enigneering. Rev Mater Res. 2025;1(1):100010.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/y079843774-dba885be-eb4a-4d18-9ead-ef274b09529f">
					  <unstructured_citation>Chávez-Angel E, Eriksen MB, Castro-Alvarez A, Garcia JH, Botifoll M, Avalos-Ovando O, et al. Applied artificial intelligence in materials science and material design. Adv Intell Syst. 2025;7(8):2400986.</unstructured_citation>
						 <doi>10.1002/aisy.202400986</doi> 					</citation>
          					<citation key="rk-10.68159/y079843774-276c07e0-42ec-4119-87ae-59486409f014">
					  <unstructured_citation>Bai X, Zhang X. Artificial intelligence-powered materials science. Nano Micro Lett. 2025;17:135.</unstructured_citation>
						 <doi>10.1007/s40820-024-01634-8</doi> 					</citation>
          					<citation key="rk-10.68159/y079843774-46c0f156-53b5-47ff-a706-cbe7da49353c">
					  <unstructured_citation>Nematov D, Hojamberdiev M. Machine learning-driven materials discovery: Unlocking next-generation functional materials – a review. Comput Condens Matter. 2025;45:e01139.</unstructured_citation>
						 <doi>10.1016/j.cocom.2025.e01139</doi> 					</citation>
          					<citation key="rk-10.68159/y079843774-23c2ee22-7c82-40dc-b432-c33dfea65f94">
					  <unstructured_citation>Alosious S, Jiang M, Luo T. Computation and machine learning for materials: Past, present, and future perspectives. MRS Bull. 2025;50(10):1212-24.</unstructured_citation>
						 <doi>10.1557/s43577-025-00959-y</doi> 					</citation>
          					<citation key="rk-10.68159/y079843774-c56a1a56-07f4-4cdb-ba3b-7b2eaebf3fe9">
					  <unstructured_citation>Unni R, Zhou M, Wiecha PR, Zheng Y. Advancing materials science through next-generation machine learning. Curr Opin Solid State Mater Sci. 2024;30(3):101157.</unstructured_citation>
						 <doi>10.1016/j.cossms.2024.101157</doi> 					</citation>
          					<citation key="rk-10.68159/y079843774-40299245-ac68-4eb0-8b1c-36efbc9fafc8">
					  <unstructured_citation>Zheng Y. Artificial intelligence and machine learning for materials. Curr Opin Solid State Mater Sci. 2024;34:101202.</unstructured_citation>
						 <doi>10.1016/j.cossms.2024.101202</doi> 					</citation>
          					<citation key="rk-10.68159/y079843774-2ac665d0-37d3-4780-a79f-e3d991268acc">
					  <unstructured_citation>Zhou QH, Chen X, Wang JL. Machine learning assisted material discovery: A small data approach. Acc Mater Res. 2025;6(6):685-94.</unstructured_citation>
						 <doi>10.1021/accountsmr.1c00236</doi> 					</citation>
          					<citation key="rk-10.68159/y079843774-6a2f4d9d-67b6-47bc-9176-ea86fd8ee5fb">
					  <unstructured_citation>Liu Y, Jovanovic M, Mallayya K, Maddox WJ, Wilson AG, Klemenz S, et al. Materials expert-artificial intelligence for materials discovery. Commun Mater. 2025;6(1):212.</unstructured_citation>
						 <doi>10.1038/s43246-025-00928-7</doi> 					</citation>
          					<citation key="rk-10.68159/y079843774-73c36a15-19fe-414c-bfe5-4a1b59c8673d">
					  <unstructured_citation>Vinuesa R, Azizpour H, Leite I, Balaam M, Dignum V, Domisch S, et al. The role of artificial intelligence in achieving the sustainable development goals. Nat Commun. 2020;11(1):233.</unstructured_citation>
						 <doi>10.1038/s41467-019-14108-y</doi> 					</citation>
          					<citation key="rk-10.68159/y079843774-b0879f74-9aca-48f6-832d-dbbc191ee9e1">
					  <unstructured_citation>Jarrahi M, Karami A, Conway P, Memariani A, Lutz C. Navigating the muddy waters of bias in artificial intelligence research: Understanding divergent meanings and conceptions. Technol Soc. 2026;84:103127.</unstructured_citation>
						 <doi>10.1016/j.techsoc.2025.103127</doi> 					</citation>
          					<citation key="rk-10.68159/y079843774-70de0fc3-ea23-4342-a838-15c63e7db5bb">
					  <unstructured_citation>Hasanzadeh F, Josephson CB, Waters G, Adedinsewo D, Azizi Z, White JA. Bias recognition and mitigation strategies in artificial intelligence healthcare applications. npj Digit Med. 2025;8(1):154.</unstructured_citation>
						 <doi>10.1038/s41746-025-01503-7</doi> 					</citation>
          					<citation key="rk-10.68159/y079843774-7818128a-cdbe-4941-ab45-ea3dac5a2171">
					  <unstructured_citation>Falaguera MJ, McDonagh EM, Ochoa D, Rusina PV, Roldan-Romero JM, Hulcoop DG, et al. Temporal trends in evidence supporting novel drug target discovery. Nat Commun. 2026;17(1):492.</unstructured_citation>
						 <doi>10.1038/s41467-025-67180-y</doi> 					</citation>
          					<citation key="rk-10.68159/y079843774-8504c713-e061-46be-94e5-ef6304200512">
					  <unstructured_citation>Ding X, Shang B, Xie C, Xin J, Yu F. Artificial intelligence in the COVID-19 pandemic: Balancing benefits and ethical challenges in China’s response. Humanit Soc Sci Commun. 2025;12(1):245.</unstructured_citation>
						 <doi>10.1057/s41599-025-04564-x</doi> 					</citation>
          					<citation key="rk-10.68159/y079843774-204a3ed4-61fd-47b8-94f4-943d2dd8b361">
					  <unstructured_citation>Van Busum K, Fang S. Interactive mitigation of biases in machine learning models for undergraduate student admissions. AI. 2025;6(7):152.</unstructured_citation>
						 <doi>10.3390/ai6070152</doi> 					</citation>
          					<citation key="rk-10.68159/y079843774-eb0b5d84-15ca-465c-a0a6-9fef0f0da687">
					  <unstructured_citation>Younas M, El-Dakhs DA, Noor U. The impact of artificial intelligence-based learning tools in academic innovation: A review of deep seek, GPT, and gemini (2020–2025). Front Educ. 2025;10:1689205.</unstructured_citation>
						 <doi>10.3389/feduc.2025.1689205</doi> 					</citation>
          					<citation key="rk-10.68159/y079843774-191dbf6f-6686-4f2d-840d-59c336305cef">
					  <unstructured_citation>Afreen J, Mohaghegh M, Doborjeh M. Systematic literature review on bias mitigation in generative AI. AI Ethics. 2025;5(4):4789-841.</unstructured_citation>
						 <doi>10.1007/s43681-025-00721-9</doi> 					</citation>
          				</citation_list>
			</journal_article>
		</journal>
	</body>
</doi_batch>
