<?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-usQI-1793858369-h788480315</doi_batch_id>
		<timestamp>1793858369</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>2</issue>
			</journal_issue>
			<journal_article publication_type="full_text">
				<titles>
					<title>Multi-Model and Hybrid AI Systems in Materials Research — Architectures and Trade-Offs</title>
				</titles>
								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>Nguyen Thanh</given_name>
            <surname>Huy</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Le Thi</given_name>
            <surname>Bich</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Pham Quang</given_name>
            <surname>Minh</surname>
					</person_name>
          				</contributors>
								<publication_date>
					<year>2023</year>
				</publication_date>
				<doi_data>
					<doi>10.68159/h788480315</doi>
					<resource>https://iamrp.net/pub/journal/1/article/h788480315</resource>
				</doi_data>
				<citation_list>
          					<citation key="rk-10.68159/h788480315-44137512-5485-4b02-b04c-1845ec9fff87">
					  <unstructured_citation>Zivic FT, 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/h788480315-9dfdb905-0a4f-44d6-bffa-40d85289e875">
					  <unstructured_citation>Abdelal G, Chan C-W, McLoone S. Critical review of recent advances in ai-enhanced sem and eds techniques for metallic microstructure characterization. Appl Sci. 2026;16(2):975.</unstructured_citation>
						 <doi>10.3390/app16020975</doi> 					</citation>
          					<citation key="rk-10.68159/h788480315-35c2279d-43bf-4331-9c7c-c0d24bb60410">
					  <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/h788480315-a7cbeb64-02b2-405f-b9bc-694b4ea348ca">
					  <unstructured_citation>Plevris V, Hosamo H. Responsible ai in structural engineering: A framework for ethical use. Front Built Environ. 2025;11:1612575.</unstructured_citation>
						 <doi>10.3389/fbuil.2025.1612575</doi> 					</citation>
          					<citation key="rk-10.68159/h788480315-091bf4ec-1cc7-4f67-99ea-29daf1652cf3">
					  <unstructured_citation>Li X, Sun Y, Sha Z. Llm4cad: Multimodal large language models for three-dimensional computer-aided design generation. J Comput Inf Sci Eng. 2025;25(2):021005.</unstructured_citation>
						 <doi>10.1115/1.4067085</doi> 					</citation>
          					<citation key="rk-10.68159/h788480315-95edfbbd-e3a6-4353-ac11-be757f228c9e">
					  <unstructured_citation>Peivaste I, Belouettar S, Mercuri F, Fantuzzi N, Dehghani H, Izadi R, et al. Artificial intelligence in materials science and engineering: Current landscape, key challenges, and future trajectories. Compos Struct. 2025;372:119419.</unstructured_citation>
						 <doi>10.1016/j.compstruct.2025.119419</doi> 					</citation>
          					<citation key="rk-10.68159/h788480315-3297a9d7-3442-4e04-b5d2-75503e064bbc">
					  <unstructured_citation>Cao Y, Fu H, Lu J, Chen Y, Jing T, Fan X, et al. Artificial intelligence empowered new materials: Discovery, synthesis, prediction to validation. Nano Micro Lett. 2026;18:109.</unstructured_citation>
						 <doi>10.1007/s40820-025-01945-4</doi> 					</citation>
          					<citation key="rk-10.68159/h788480315-8f2495cd-950b-4848-b289-59277997a8a9">
					  <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/h788480315-64f00776-f5ad-43a0-8c8f-dd0615ce6101">
					  <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).</unstructured_citation>
						 <doi>10.1002/aisy.202400986</doi> 					</citation>
          					<citation key="rk-10.68159/h788480315-18d19b18-8e23-4e33-a9d4-c6b0d65d57c4">
					  <unstructured_citation>Hong H, Kim S, Lee J, Kim SS. A comprehensive review of artificial intelligence applications in composite materials: Predictive, generative, and automation approaches. Int J AI Mater Des. 2025;2(3):1-30.</unstructured_citation>
						 <doi>10.36922/IJAMD025210016</doi> 					</citation>
          					<citation key="rk-10.68159/h788480315-0ad6d80d-b661-49b5-975c-c113f75c9009">
					  <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/h788480315-6594860c-0c80-4660-9fd1-df8e25d498b1">
					  <unstructured_citation>Gomez-Romero P, Pokhriyal A, Rueda-García D, Bengoa LN, González-Gil RM. Hybrid materials: A metareview. Chem Mater. 2024;36(1):8-27.</unstructured_citation>
						 <doi>10.1021/acs.chemmater.3c01878</doi> 					</citation>
          					<citation key="rk-10.68159/h788480315-498af546-ab48-4ccc-87a1-7f98acac0c86">
					  <unstructured_citation>Zhou Q, Chen X, Wang J. 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/h788480315-10532450-11d4-4f97-af09-f249c7b43318">
					  <unstructured_citation>Ting JY, Barnard AS. What to make and how to make it: Combining machine learning and statistical learning to design new materials. Adv Intell Discov. 2025.</unstructured_citation>
						 <doi>10.1002/aidi.202500033</doi> 					</citation>
          					<citation key="rk-10.68159/h788480315-88260831-1fe9-4afa-867a-b333560fcc34">
					  <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/h788480315-92309301-2f64-445e-9e5c-d70635123c2d">
					  <unstructured_citation>Shu Y, Miao N, Sun Z. Machine learning-enabled optoelectronic material discovery: A comprehensive review. J Mater Inf. 2025;5:36.</unstructured_citation>
						 <doi>10.20517/jmi.2025.13</doi> 					</citation>
          					<citation key="rk-10.68159/h788480315-691c1f55-978e-4abd-8493-04e072c4faeb">
					  <unstructured_citation>Thomas K, Khandakar A, Chelvanathan P, Aissa B, Hossain MI. Enhanced prediction and optimization of thin metal film optical properties using optimized ensemble learning models. Sci Rep. 2025;15:43523.</unstructured_citation>
						 <doi>10.1038/s41598-025-27524-6</doi> 					</citation>
          					<citation key="rk-10.68159/h788480315-c1b85485-a381-4400-82a4-c01973428ff5">
					  <unstructured_citation>Michaloglou A, Papadimitriou I, Gialampoukidis I, Vrochidis S, Kompatsiaris I. Physics-informed neural networks in materials modeling and design: A review. Arch Comput Methods Eng. 2025.</unstructured_citation>
						 <doi>10.1007/s11831-025-10448-9</doi> 					</citation>
          					<citation key="rk-10.68159/h788480315-680c90c6-b66b-4c1f-ad61-a5978b4cdd31">
					  <unstructured_citation>Ren Z, Zhou S, Liu D, Liu Q. Physics-informed neural networks: A review of methodological evolution, theoretical foundations, and interdisciplinary frontiers toward next-generation scientific computing. Appl Sci. 2025;15(14):8092.</unstructured_citation>
						 <doi>10.3390/app15148092</doi> 					</citation>
          					<citation key="rk-10.68159/h788480315-5655eb16-e941-41ed-afef-e1c56a5fd9e2">
					  <unstructured_citation>Liu Z, Liu Y, Yan X, Liu W, Nie H, Guo S, et al. Automatic network structure discovery of physics informed neural networks via knowledge distillation. Nat Commun. 2025;16:9558.</unstructured_citation>
						 <doi>10.1038/s41467-025-64624-3</doi> 					</citation>
          					<citation key="rk-10.68159/h788480315-07f7cf20-6be3-46e7-a4cc-d26b70938093">
					  <unstructured_citation>Malashin I, Tynchenko V, Gantimurov A, Nelyub V, Borodulin A. Physics-informed neural networks in polymers: A review. Polymers. 2025;17(8):1108.</unstructured_citation>
						 <doi>10.3390/polym17081108</doi> 					</citation>
          					<citation key="rk-10.68159/h788480315-6d6cc421-ae72-4fa0-8773-bdaa2c6bb5e1">
					  <unstructured_citation>Moosavi SM, Jablonka KM, Smit B. The role of machine learning in the understanding and design of materials. J Am Chem Soc. 2020;142(48):20273-87.</unstructured_citation>
						 <doi>10.1021/jacs.0c09105</doi> 					</citation>
          					<citation key="rk-10.68159/h788480315-5ceda0f8-b8f6-40d2-acbe-683d3fb20dee">
					  <unstructured_citation>Cai J, Chu X, Xu K, Li H, Wei J. Machine learning-driven new material discovery. Nanoscale Adv. 2020;2:3115-30.</unstructured_citation>
						 <doi>10.1039/D0NA00388C</doi> 					</citation>
          					<citation key="rk-10.68159/h788480315-2dd5ec28-c892-4d91-bf49-f59fef8b6f0b">
					  <unstructured_citation>Mishra A, Kompella L, Sanagavarapu LM, Varam S. Ensemble-based machine learning models for phase prediction in high entropy alloys. Comput Mater Sci. 2022;210:111025.</unstructured_citation>
						 <doi>10.1016/j.commatsci.2022.111025</doi> 					</citation>
          					<citation key="rk-10.68159/h788480315-d0371bd9-f404-41fd-b590-75ff49a3800b">
					  <unstructured_citation>Riebesell J, Goodall REA, 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>
						 <doi>10.1038/s42256-025-01055-1</doi> 					</citation>
          					<citation key="rk-10.68159/h788480315-1a426051-a1c7-41c5-b08e-a161859a0b29">
					  <unstructured_citation>Yuan B, Choo CS, Yeo LY, Wang Y, Yang Z, Guan Q, et al. Physics-informed machine learning in geotechnical engineering: A direction paper. Geomech Geoeng. 2025;20:1128-59.</unstructured_citation>
						 <doi>10.1080/17486025.2025.2502029</doi> 					</citation>
          					<citation key="rk-10.68159/h788480315-8ff3619c-1f70-4598-8b26-ccc2b30b0403">
					  <unstructured_citation>Tian M, Mu H, Liu T, Li M, Ding D, Zhao J. Physics-informed machine learning-based real-time long-horizon temperature fields prediction in metallic additive manufacturing. Commun Eng. 2025;4:168.</unstructured_citation>
						 <doi>10.1038/s44172-025-00501-7</doi> 					</citation>
          				</citation_list>
			</journal_article>
		</journal>
	</body>
</doi_batch>
