<?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-5Z5c-1788905500-c568454784</doi_batch_id>
		<timestamp>1788905500</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>2022</year>
				</publication_date>
				<journal_volume>
					<volume>1</volume>
				</journal_volume>
				<issue>1</issue>
			</journal_issue>
			<journal_article publication_type="full_text">
				<titles>
					<title>The Coordination Problem in Multi-Model Materials AI Pipelines</title>
				</titles>
								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>Li</given_name>
            <surname>Zhang</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Wei</given_name>
            <surname>Chen</surname>
					</person_name>
          				</contributors>
								<publication_date>
					<year>2022</year>
				</publication_date>
				<doi_data>
					<doi>10.68159/c568454784</doi>
					<resource>https://iamrp.net/pub/journal/1/article/c568454784</resource>
				</doi_data>
				<citation_list>
          					<citation key="rk-10.68159/c568454784-f52bfbfa-fb13-4f91-83c2-703fac7a9f99">
					  <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):032001.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/c568454784-6251b9bf-550d-4efc-9d6a-86afd772bd97">
					  <unstructured_citation>Bai X, Zhang X. Artificial intelligence-powered materials science. Nano-Micro Lett. 2025;17(1):135.</unstructured_citation>
						 <doi>10.1007/s40820-024-01634-8</doi> 					</citation>
          					<citation key="rk-10.68159/c568454784-252110db-b519-425a-b362-9abcb5ba8055">
					  <unstructured_citation>Zivic F, Kaplarevic Malisic A, Grujovic N, Stojanovic B, Ivanovic M. Materials informatics: a review of AI and machine learning tools, platforms, data repositories, and applications to architectured porous materials. Mater Today Commun. 2025;48:113525.</unstructured_citation>
						 <doi>10.1016/j.mtcomm.2025.113525</doi> 					</citation>
          					<citation key="rk-10.68159/c568454784-a1dd119b-1d81-43a7-bfcb-1f98acd5bba8">
					  <unstructured_citation>Back S, Aspuru-Guzik A, Ceriotti M, Gryn’ova G, Grzybowski B, Gu GH, et al. Accelerated chemical science with AI. Digit Discov. 2024;3(1):23-33.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/c568454784-3cb30200-9cd7-4c6e-9017-32a1b5b189e8">
					  <unstructured_citation>Wang F, Jiang S, Li J. The AI-driven transformation in new materials manufacturing and the development of intelligent sports. Appl Sci. 2025;15(10):5667.</unstructured_citation>
						 <doi>10.3390/app15105667</doi> 					</citation>
          					<citation key="rk-10.68159/c568454784-2f7be6c4-d335-4fa0-9b9e-9d53065c7ea6">
					  <unstructured_citation>Hegi H, Heitz J, Kredel R. Sensor-based augmented visual feedback for coordination training in healthy adults: a scoping review. Front Sports Act Living. 2023;5:1145247.</unstructured_citation>
						 <doi>10.3389/fspor.2023.1145247</doi> 					</citation>
          					<citation key="rk-10.68159/c568454784-29c9699b-d59b-4925-a856-922455f20303">
					  <unstructured_citation>Mohammadi M, Tajik E, Martinez-Maldonado R, Sadiq SS, Tomaszewski W, Khosravi H. Artificial intelligence in multimodal learning analytics: a systematic literature review. Comput Educ Artif Intell. 2025;8:100426.</unstructured_citation>
						 <doi>10.1016/j.caeai.2025.100426</doi> 					</citation>
          					<citation key="rk-10.68159/c568454784-dc147bed-ca4b-4360-8d4d-bf12caaa0fc0">
					  <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(3):949-59.</unstructured_citation>
						 <doi>10.26599/NR.2025.94908095</doi> 					</citation>
          					<citation key="rk-10.68159/c568454784-1f879340-8f7f-44ae-ae82-dde27dce3f7f">
					  <unstructured_citation>Al-kfairy M, Mustafa D, Kshetri N, Insiew M, Alfandi O. Ethical challenges and solutions of generative AI: an interdisciplinary perspective. Informatics. 2024;11(3):58.</unstructured_citation>
						 <doi>10.3390/informatics11030058</doi> 					</citation>
          					<citation key="rk-10.68159/c568454784-74fe08c6-c4af-4b3c-bac1-f310542b4711">
					  <unstructured_citation>Uddin M, Arfeen SU, Alanazi F, Hussain S, Mazhar T, Rahman MA. A critical analysis of generative AI: challenges, opportunities, and future research directions. Arch Comput Methods Eng. 2025;32(4):1-25.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/c568454784-0fa9d6cf-b120-4c27-859e-20cc91f7aaf9">
					  <unstructured_citation>Lekadir K, Frangi AF, Porras AR, Glocker B, Cintas CC, Langlotz CP, et al. FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare. BMJ. 2025;388:e081554.</unstructured_citation>
						 <doi>10.1136/bmj-2024-081554</doi> 					</citation>
          					<citation key="rk-10.68159/c568454784-499a2eb4-52b8-4599-b913-b59cc02532b0">
					  <unstructured_citation>Ueda D, Kakinuma T, Fujita S, Kamishima Y, Yanagawa M, Sato J, et al. Fairness of artificial intelligence in healthcare: review and recommendations. Jpn J Radiol. 2024;42(1):3-15.</unstructured_citation>
						 <doi>10.1007/s11604-023-01474-3</doi> 					</citation>
          					<citation key="rk-10.68159/c568454784-a12799b0-726d-40fd-8c6f-e8771d498030">
					  <unstructured_citation>Prabhu DF, Gurupur V, Stone A, Trader E. Integrating artificial intelligence, electronic health records, and wearables for predictive, patient-centered decision support in healthcare. Healthcare. 2025;13(21):2753.</unstructured_citation>
						 <doi>10.3390/healthcare13212753</doi> 					</citation>
          					<citation key="rk-10.68159/c568454784-eb48327a-24b6-4965-aa4a-60e0a6cc1e65">
					  <unstructured_citation>Chowdhury SZ, Stevens S, Wu C, Woodward C, Andrews T, Ashall-Payne L, et al. An age-old problem or an old-age problem? A UK survey of attitudes, historical use and recommendations by healthcare professionals to use healthcare apps. BMC Geriatr. 2023;23(1):110.</unstructured_citation>
						 <doi>10.1186/s12877-023-03772-x</doi> 					</citation>
          					<citation key="rk-10.68159/c568454784-65dc23a4-5559-4e27-9a34-1b8490a897bc">
					  <unstructured_citation>Fang S, Hu YH. Open the door to the atomic world by single-molecule atomic force microscopy. Matter. 2021;4(4):1189-223.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/c568454784-a18787d0-f056-4aa3-95a0-68b0bfadc318">
					  <unstructured_citation>Awuni S, Adarkwah F, Ofori BD, Purwestri RC, Huertas Bernal DC, Hajek M. Managing the challenges of climate change mitigation and adaptation strategies in Ghana. Heliyon. 2023;9(5):e15491.</unstructured_citation>
						 <doi>10.1016/j.heliyon.2023.e15491</doi> 					</citation>
          					<citation key="rk-10.68159/c568454784-a7c3eee9-6a56-425c-9385-1ae5999d85f9">
					  <unstructured_citation>Zhang Y, Xie S, Zeng Z, Tang BZ. Functional scaffolds from AIE building blocks. Matter. 2020;3(6):1862-92.</unstructured_citation>
						 <doi>10.1016/j.matt.2020.09.017</doi> 					</citation>
          					<citation key="rk-10.68159/c568454784-3987ec80-ec77-4bcd-bc51-7eb57b16f8c4">
					  <unstructured_citation>Ghaffarian S, Taghikhah FR, Maier HR. Explainable artificial intelligence in disaster risk management: achievements and prospective futures. Int J Disaster Risk Reduct. 2023;98:104123.</unstructured_citation>
						 <doi>10.1016/j.ijdrr.2023.104123</doi> 					</citation>
          					<citation key="rk-10.68159/c568454784-248437bb-dc3b-42ab-b95b-332e7f86d89b">
					  <unstructured_citation>GBD 2021 Diabetes Collaborators. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2023;402(10397):203-34.</unstructured_citation>
						 <doi>10.1016/S0140-6736(23)01301-6</doi> 					</citation>
          					<citation key="rk-10.68159/c568454784-552b80d3-2731-4f9b-89e6-8978cb18eb91">
					  <unstructured_citation>Bhore SS, Natraj NA, Hallur GG. Bayesian-driven autonomous defense adaptive consensus optimisation for blockchain networks. Sci Rep. 2025;15(1):2158.</unstructured_citation>
						 <doi>10.1038/s41598-025-31929-8</doi> 					</citation>
          					<citation key="rk-10.68159/c568454784-0051b05d-6350-4b5b-a6fe-f9738e0cfdcd">
					  <unstructured_citation>Silver P, Furey J, Heiman-Patterson T. Gastrointestinal symptoms in ALS: evidence for enteric nervous system involvement and clinical implications. Muscle Nerve. 2024;70(1):3-9.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/c568454784-8f9ae021-e2ac-40a3-835f-fb4ebb40d29a">
					  <unstructured_citation>Meijboom K, Ansodaria AV, Bamidele N, Eisenberg J, Sontheimer E, Brown R. Advanced base and prime editing strategies to correct common ALS-causing SOD1 mutations. Muscle Nerve. 2025;71(S1):S1-S97.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/c568454784-bd10bfac-9ec8-4143-a44b-d22a497f9f1d">
					  <unstructured_citation>Zheng H, Luo Z, He K, Zhou W, Kong Z, Dong J, et al. KT-LLM: an evidence-grounded and sequence text framework for auditable kidney transplant modeling. npj Digit Med. 2025;8(1):23.</unstructured_citation>
						 <doi>10.1038/s41746-025-02323-5</doi> 					</citation>
          					<citation key="rk-10.68159/c568454784-48d9e0b7-6310-401a-8fd5-d01cc3f2861a">
					  <unstructured_citation>Chakraborty S, Björk J, Dahlqvist M, Rosen J, Heintz F. A survey of AI-supported materials informatics. Comput Sci Rev. 2025;59:100845.</unstructured_citation>
						 <doi>10.1016/j.cosrev.2025.100845</doi> 					</citation>
          					<citation key="rk-10.68159/c568454784-baee1cc2-5bbf-4dd7-8bc0-f7b56296fb48">
					  <unstructured_citation>Uddin M, Rahman MA, Hussain S, Alanazi F, Mazhar T, Arfeen SU. A critical analysis of generative AI: challenges, opportunities, and future research directions. Arch Comput Methods Eng. 2025;32(2):1-20.</unstructured_citation>
											</citation>
          				</citation_list>
			</journal_article>
		</journal>
	</body>
</doi_batch>
