<?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-j7qK-1792514633-w958689176</doi_batch_id>
		<timestamp>1792514633</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>The Problem of Scientific Consensus in AI-Driven Materials Science—Conceptual Approaches: A Review Study</title>
				</titles>
								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>Andrei</given_name>
            <surname>Popescu</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Mihai</given_name>
            <surname>Ionescu</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Elena</given_name>
            <surname>Stan</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Sorin</given_name>
            <surname>Dumitrescu</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Irina</given_name>
            <surname>Pavel</surname>
					</person_name>
          				</contributors>
								<publication_date>
					<year>2026</year>
				</publication_date>
				<doi_data>
					<doi>10.68159/w958689176</doi>
					<resource>https://iamrp.net/pub/journal/1/article/w958689176</resource>
				</doi_data>
				<citation_list>
          					<citation key="rk-10.68159/w958689176-fafc3de8-d80f-4566-943a-c7781357d716">
					  <unstructured_citation>Yu B, Shu F. The Matthew effect in China’s social sciences and humanities research: A comparative analysis of CSSCI and SSCI. Scientometrics. 2023;128(11):6177-93.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w958689176-18c05bdb-6881-4e43-a70d-5db8113091f9">
					  <unstructured_citation>Kuhn TS. The structure of scientific revolutions. 4th ed. Chicago: University of Chicago Press.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w958689176-9842efad-5d3a-4fbb-993c-cf5b85e8bef2">
					  <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/w958689176-3df6f198-fb93-4c89-8032-634c6bcd1fc6">
					  <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/w958689176-618d66a1-ae0c-4ea8-bd9d-af8f637b6c38">
					  <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/w958689176-9c9a5e63-47b3-488e-a065-af1657184ec8">
					  <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/w958689176-4234383c-a79c-469e-8e8b-d20966cc7484">
					  <unstructured_citation>Morgan D, Jacobs R. Opportunities and challenges for machine learning in materials science. Annu Rev Mater Res. 2020;50(1):71-103.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w958689176-99b5651f-1733-4723-ac2e-d060dac848e9">
					  <unstructured_citation>Šešelja D. Some lessons from simulations of scientific disagreements. Synthese. 2021;198(Suppl 25):6143-58.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w958689176-410d0947-ed97-418b-8106-364b5c3659c3">
					  <unstructured_citation>Lamers WS, Boyack K, Larivière V, Sugimoto CR, van Eck NJ, Waltman L, et al. Investigating disagreement in the scientific literature. eLife. 2021;10:e72737.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w958689176-391371cf-5026-43e3-9ca5-f6d970e24d1e">
					  <unstructured_citation>Michelini M, Javier O, Houkes W, Šešelja D, Straßer C. Scientific disagreements and the diagnosticity of evidence: How too much data may lead to polarization. J Artif Soc Soc Simul. 2023.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w958689176-ee52b281-754c-43dd-b4fe-9744c166e923">
					  <unstructured_citation>Hattrick-Simpers JR, Zakutayev A, Barron SC, Trautt ZT, Nguyen N, Choudhary K, et al. An inter-laboratory study of Zn–Sn–Ti–O thin films using high-throughput experimental methods. ACS Comb Sci. 2019;21(5):350-61.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w958689176-760bac0d-b0bb-4cf3-b8a6-a0d469614007">
					  <unstructured_citation>Dunn A, Wang Q, Ganose A, Dopp D, Jain A. Benchmarking materials property prediction methods: The Matbench test set and automatminer reference algorithm. npj Comput Mater. 2020;6(1):138.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w958689176-adce29f9-a8fa-4dd6-9f34-f6ed61b9b4f1">
					  <unstructured_citation>Liu Y, He X, Mo Y. Discrepancies and error evaluation metrics for machine learning interatomic potentials. npj Comput Mater. 2023;9(1):174.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w958689176-7400ac48-3eed-49ff-8ba4-7d7869ca6fc4">
					  <unstructured_citation>Li K, DeCost B, Choudhary K, Greenwood M, Hattrick-Simpers J. A critical examination of robustness and generalizability of machine learning prediction of materials properties. npj Comput Mater. 2023;9(1):55.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w958689176-cd7deb75-44f8-4e31-aaba-e5d29b41c113">
					  <unstructured_citation>Choudhary K, Wines D, Li K, Garrity KF, Gupta V, Romero AH, et al. JARVIS-Leaderboard: A large scale benchmark of materials design methods. npj Comput Mater. 2024;10(1):93.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w958689176-ccbf4f1b-04c6-49cb-be48-3e8aeb9962c3">
					  <unstructured_citation>Zhang H, Chen W, Iyer A, Apley DW, Chen W. Uncertainty-aware mixed-variable machine learning for materials design. Sci Rep. 2022;12(1):19760.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w958689176-9121910c-e3d4-41f8-b43f-e0d10feb94a4">
					  <unstructured_citation>Riebesell J, Goodall RE, Benner P, Chiang Y, Deng B, Ceder G, et al. A framework to evaluate machine learning crystal stability predictions. Nat Mach Intell. 2025;7(6):836-47.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w958689176-f13920ad-2970-4dd0-a162-546b9ed2a77f">
					  <unstructured_citation>Jha D, Choudhary K, Tavazza F, Liao WK, Choudhary A, Campbell C, et al. Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning. Nat Commun. 2019;10(1):5316.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w958689176-44265a17-6a8c-43de-b4cd-236e59d0d6f6">
					  <unstructured_citation>Gupta V, Choudhary K, Tavazza F, Campbell C, Liao WK, Choudhary A, et al. Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data. Nat Commun. 2021;12(1):6595.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w958689176-83ff7bba-8194-4496-939d-43951de3feb2">
					  <unstructured_citation>Li C, Zheng K. Methods, progresses, and opportunities of materials informatics. InfoMat. 2023;5(8):e12425.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w958689176-42efe5c1-8315-40ae-ab52-760ca07c4b48">
					  <unstructured_citation>Channing G, Ghosh A. AI for scientific discovery is a social problem. Patterns. 2026;7(3).</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w958689176-9661340c-a309-457b-9e3d-49f12aea5ed6">
					  <unstructured_citation>Bommasani R, Arora S, Chayes J, Choi Y, Cuéllar MF, Fei-Fei L, et al. Advancing science-and evidence-based AI policy. Science. 2025;389(6759):459-61.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w958689176-4a897065-3c3f-439b-bbb7-61730db8e7b7">
					  <unstructured_citation>Häse F, Aldeghi M, Hickman RJ, Roch LM, Christensen M, Liles E, et al. Olympus: A benchmarking framework for noisy optimization and experiment planning. Mach Learn Sci Technol. 2021;2(3):035021.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w958689176-17fd1123-d7a0-4bdd-8678-cd4c1a1e841b">
					  <unstructured_citation>Aldeghi M, Häse F, Hickman RJ, Tamblyn I, Aspuru-Guzik A. Golem: An algorithm for robust experiment and process optimization. Chem Sci. 2021;12(44):14792-807.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w958689176-dfbe398c-9318-44b0-9a91-844a79107447">
					  <unstructured_citation>Choudhary K, DeCost B, Tavazza F. Machine learning with force-field-inspired descriptors for materials: Fast screening and mapping energy landscape. Phys Rev Mater. 2018;2(8):083801.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w958689176-31fff51d-0aa1-48d9-ac47-bcb488fec331">
					  <unstructured_citation>Wexler RB, Gautam GS, Stechel EB, Carter EA. Factors governing oxygen vacancy formation in oxide perovskites. J Am Chem Soc. 2021;143(33):13212-27.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w958689176-0d37a5fc-be6a-42d7-b70e-52746a16efde">
					  <unstructured_citation>Tavazza F, DeCost B, Choudhary K. Uncertainty prediction for machine learning models of material properties. ACS Omega. 2021;6(48):32431-40.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w958689176-394e3291-9895-4e09-9c51-6e67ce908eb5">
					  <unstructured_citation>Li X, Guo Y. Paradigm shifts from data-intensive science to robot scientists. Sci Bull. 2025;70(1):14-8.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w958689176-d4976007-c12b-4e09-a27e-063d48c77da3">
					  <unstructured_citation>Kapoor S, Cantrell EM, Peng K, Pham TH, Bail CA, Gundersen OE, et al. REFORMS: Consensus-based recommendations for machine-learning-based science. Sci Adv. 2024;10(18):eadk3452.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w958689176-988cc912-4689-4a8d-aa50-ed58f2f940fd">
					  <unstructured_citation>Bommasani R. NeurIPS should lead scientific consensus on AI policy. arXiv preprint arXiv:2510.00075. 2025 Sep 30.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w958689176-3b8f96bd-aedb-4e52-8aa9-25e5dac5b398">
					  <unstructured_citation>Marconi L, Cabitza F. Show and tell: A critical review on robustness and uncertainty for a more responsible medical AI. Int J Med Inform. 2025;202:105970.</unstructured_citation>
											</citation>
          				</citation_list>
			</journal_article>
		</journal>
	</body>
</doi_batch>
