<?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-kNWh-1791491023-n873145769</doi_batch_id>
		<timestamp>1791491023</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 Computational and Data-Driven Materials Engineering</full_title>
				<abbrev_title>J. Comput. Data-Driven Mater. Eng.</abbrev_title>
				<issn>3149-9368</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>A Decade of Data-Driven Materials Engineering: Progress, Remaining Gaps, and Unlearned Lessons</title>
				</titles>
								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>Mohamed</given_name>
            <surname>Salah</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Youssef</given_name>
            <surname>Karim</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Ahmed</given_name>
            <surname>Nabil</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Mahmoud</given_name>
            <surname>Adel</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Karim</given_name>
            <surname>Hassan</surname>
					</person_name>
          				</contributors>
								<publication_date>
					<year>2026</year>
				</publication_date>
				<doi_data>
					<doi>10.68159/n873145769</doi>
					<resource>https://iamrp.net/pub/journal/2/article/n873145769</resource>
				</doi_data>
				<citation_list>
          					<citation key="rk-10.68159/n873145769-385ce052-a0de-478c-9548-268e8e874e3f">
					  <unstructured_citation>Xie T, Grossman JC. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys Rev Lett. 2018;120(14):145301.</unstructured_citation>
						 <doi>10.1103/PhysRevLett.120.145301</doi> 					</citation>
          					<citation key="rk-10.68159/n873145769-4125e99c-07f5-4873-ba41-df4b4b72d4b6">
					  <unstructured_citation>Chen C, Ye W, Zuo Y, Zheng C, Ong SP. Graph networks as a universal machine learning framework for molecules and crystals. Chem Mater. 2019;31(9):3564-72.</unstructured_citation>
						 <doi>10.1021/acs.chemmater.9b01294</doi> 					</citation>
          					<citation key="rk-10.68159/n873145769-bf0c329b-5f1d-45d9-a94d-ea909a25d42b">
					  <unstructured_citation>Shi X, Zhou L, Huang Y, Wu Y, Hong Z. A review on the applications of graph neural networks in materials science at the atomic scale. MGE Adv. 2024;2(2).</unstructured_citation>
						 <doi>10.1002/mgea.50</doi> 					</citation>
          					<citation key="rk-10.68159/n873145769-d3259083-3f15-40e4-8a20-7a92cf3d375f">
					  <unstructured_citation>Reiser P, Neubert M, Eberhard A, Torresi L, Zhou C, Shao C, et al. Graph neural networks for materials science and chemistry. Commun Mater. 2022;3(1):93.</unstructured_citation>
						 <doi>10.1038/s43246-022-00315-6</doi> 					</citation>
          					<citation key="rk-10.68159/n873145769-41a22df0-378e-47a7-9cfb-66f81955b1f6">
					  <unstructured_citation>Zhao Z, Hu T, Bi S, Guan D, Xu S, Chen C, et al. Advances in graph neural networks for alloy design and properties predictions: A review. J Mater Inf. 2026;6:2.</unstructured_citation>
						 <doi>10.20517/jmi.2025.42</doi> 					</citation>
          					<citation key="rk-10.68159/n873145769-34dc5975-2cf7-40c9-a055-275bd7b11e16">
					  <unstructured_citation>Zeni C, Pinsler R, Zügner D, Fowler A, Horton M, Fu X, et al. A generative model for inorganic materials design. Nature. 2025;639(8055):624-32.</unstructured_citation>
						 <doi>10.1038/s41586-025-08628-5</doi> 					</citation>
          					<citation key="rk-10.68159/n873145769-f20c5847-a47c-4af2-b948-942c455ce4f3">
					  <unstructured_citation>Metni H, Ruple L, Walters LN, Torresi L, Teufel J, Schopmans H, et al. Generative models for crystalline materials. Adv Mater. 2026;38(18).</unstructured_citation>
						 <doi>10.1002/adma.202523620</doi> 					</citation>
          					<citation key="rk-10.68159/n873145769-24880645-6f16-4be8-8123-ca135690bb2a">
					  <unstructured_citation>Padbury R. Data-driven approaches to materials and process challenges: A new tool for the materials science field. Am Ceram Soc Bull. 2020;99(6):24-30.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/n873145769-e3e43f47-a644-45aa-9149-aa14a4b67a28">
					  <unstructured_citation>Ramprasad R, Batra R, Pilania G, Mannodi-Kanakkithodi A, Kim C. Machine learning in materials informatics: Recent applications and prospects. NPJ Comput Mater. 2017;3(1):54.</unstructured_citation>
						 <doi>10.1038/s41524-017-0056-5</doi> 					</citation>
          					<citation key="rk-10.68159/n873145769-e2359ce1-6bb6-4b12-9117-d2b21b62dd24">
					  <unstructured_citation>Wang Z, Sun Z, Yin H, Liu X, Wang J, Zhao H, et al. Data-driven materials innovation and applications. Adv Mater. 2022;34(36).</unstructured_citation>
						 <doi>10.1002/adma.202104113</doi> 					</citation>
          					<citation key="rk-10.68159/n873145769-2ee1c2e3-b161-4a66-ba93-d5867de647b3">
					  <unstructured_citation>Aguilar-Bejarano E, Arrieta L, Gutiérrez M, Özcan E, Woodward S, Figueredo G, et al. Explainable GNN-derived structure-property relationships in interstitial-alloy materials. Phys Chem Chem Phys. 2025;27(41):22240-50.</unstructured_citation>
						 <doi>10.1039/D5CP02208H</doi> 					</citation>
          					<citation key="rk-10.68159/n873145769-e0fc8fd2-7525-42a7-af17-cdc584cbcadc">
					  <unstructured_citation>Kassa G. Wef-GNN: A generalizable graph neural network for crystalline material property prediction [Internet]. OpenReview; 2025 [modified 2026 Feb 11; cited 2026 Jun 23]. Available from: https://openreview.net/forum?id=Hs4WbkJqsm</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/n873145769-5b89a449-9095-4b5d-a969-ec2066fcfdec">
					  <unstructured_citation>Horton MK, Huck P, Yang RX, Munro JM, Dwaraknath S, Ganose AM, et al. Accelerated data-driven materials science with the Materials Project. Nat Mater. 2025;24(10):1522-32.</unstructured_citation>
						 <doi>10.1038/s41563-025-02272-0</doi> 					</citation>
          					<citation key="rk-10.68159/n873145769-5c76906a-09c8-447f-93d9-4b693638c02e">
					  <unstructured_citation>Choudhary K, Garrity KF, Reid AC, DeCost B, Biacchi AJ, Hight Walker AR, et al. The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design. NPJ Comput Mater. 2020;6(1):173.</unstructured_citation>
						 <doi>10.1038/s41524-020-00440-1</doi> 					</citation>
          					<citation key="rk-10.68159/n873145769-a253cc80-ba64-441e-9f03-93f946497eb4">
					  <unstructured_citation>Yang S, Cho K, Merchant A, Abbeel P, Schuurmans D, Mordatch I, et al. Scalable diffusion for materials generation [Preprint]. arXiv; 2023.</unstructured_citation>
						 <doi>10.48550/arXiv.2311.09235</doi> 					</citation>
          					<citation key="rk-10.68159/n873145769-d37e3631-8525-4a37-b6a2-e11dacfa6d26">
					  <unstructured_citation>Lookman T, Liu Y, Gao Z. Materials informatics: Emergence to autonomous discovery in the age of AI. Adv Mater. 2026;38(29).</unstructured_citation>
						 <doi>10.1002/adma.202515941</doi> 					</citation>
          					<citation key="rk-10.68159/n873145769-37b21733-1b0b-4f7c-ad01-d49e9124d755">
					  <unstructured_citation>Olivetti EA, Cole JM, Kim E, Kononova O, Ceder G, Han TY, et al. Data-driven materials research enabled by natural language processing and information extraction. Appl Phys Rev. 2020;7(4):041317.</unstructured_citation>
						 <doi>10.1063/5.0021106</doi> 					</citation>
          					<citation key="rk-10.68159/n873145769-f404766a-89ca-412f-a146-cca0cf98390e">
					  <unstructured_citation>Gupta KK, Barman S, Sankar A, Dey S, Mukhopadhyay T. Artificial intelligence in materials by design: Critical review and perspectives on materials informatics to generative and agentic intelligence. Arch Comput Methods Eng. 2026;33:6015-45.</unstructured_citation>
						 <doi>10.1007/s11831-025-10486-3</doi> 					</citation>
          					<citation key="rk-10.68159/n873145769-1b68b7dd-afcf-425e-9a27-240d944478ef">
					  <unstructured_citation>Gomez-Gualdron D, de Vilas TG, Ardila K, Fajardo-Rojas JF, Pak A. Machine learning to design metal-organic frameworks: Progress and challenges from a data efficiency perspective. Mater Horiz. 2026;13:1694-715.</unstructured_citation>
						 <doi>10.1039/D5MH01467K</doi> 					</citation>
          					<citation key="rk-10.68159/n873145769-11979970-bf25-44c2-8674-fcce00d882ad">
					  <unstructured_citation>Nematov D, Hojamberdiev M. Machine learning-driven materials discovery: Unlocking next-generation functional materials-A review. Comput Condens Matter. 2025;45.</unstructured_citation>
						 <doi>10.1016/j.cocom.2025.e01139</doi> 					</citation>
          					<citation key="rk-10.68159/n873145769-1c306b4c-45c6-440e-b692-a400966c2f69">
					  <unstructured_citation>Pollice R, dos Passos Gomes G, Aldeghi M, Hickman RJ, Krenn M, Lavigne C, et al. Data-driven strategies for accelerated materials design. Acc Chem Res. 2021;54(4):849-60.</unstructured_citation>
						 <doi>10.1021/acs.accounts.0c00785</doi> 					</citation>
          					<citation key="rk-10.68159/n873145769-938487f9-0655-4e35-8a79-d1a27e26f884">
					  <unstructured_citation>Butler KT, Choudhary K, Csanyi G, Ganose AM, Kalinin SV, Morgan D. Setting standards for data driven materials science. NPJ Comput Mater. 2024;10(1):231.</unstructured_citation>
						 <doi>10.1038/s41524-024-01411-6</doi> 					</citation>
          					<citation key="rk-10.68159/n873145769-a8aef515-d307-4e67-80fc-a6dcab286580">
					  <unstructured_citation>Zivic F, Malisic AK, 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/n873145769-158be5c0-fcf1-4e94-8954-0f12343e78c5">
					  <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>
						 <doi>10.1146/annurev-matsci-070218-010015</doi> 					</citation>
          					<citation key="rk-10.68159/n873145769-3f8ae260-3ace-453a-a1f6-02abf7126c80">
					  <unstructured_citation>Jain A. Machine learning in materials research: Developments over the last decade and challenges for the future. Curr Opin Solid State Mater Sci. 2024;33:101189.</unstructured_citation>
						 <doi>10.1016/j.cossms.2024.101189</doi> 					</citation>
          					<citation key="rk-10.68159/n873145769-ec7848e8-a01d-43a3-843a-cbddb611e855">
					  <unstructured_citation>Zhang Y, Xue D, Xin S, Wang X, Zhou W, Pan X, et al. Research progress of machine learning aided titanium alloys design. Mater China. 2025;44(4):319-29.</unstructured_citation>
						 <doi>10.7502/j.issn.1674-3962.202501004</doi> 					</citation>
          					<citation key="rk-10.68159/n873145769-3e9133eb-46ea-482f-b5f2-95122ed56125">
					  <unstructured_citation>Osaro E, Mukherjee K, Colon YJ. Active learning for adsorption simulations: Evaluation, criteria analysis, and recommendations for metal-organic frameworks. Ind Eng Chem Res. 2023;62(33):13009-24.</unstructured_citation>
						 <doi>10.1021/acs.iecr.3c01589</doi> 					</citation>
          					<citation key="rk-10.68159/n873145769-955e0240-2cff-47de-a032-b749ef88180c">
					  <unstructured_citation>Lookman T, Balachandran PV, Xue D, Yuan R. Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design. NPJ Comput Mater. 2019;5(1):21.</unstructured_citation>
						 <doi>10.1038/s41524-019-0153-8</doi> 					</citation>
          					<citation key="rk-10.68159/n873145769-634cb1e5-e2c9-4d43-98f4-27aaad4cfd8c">
					  <unstructured_citation>Cao B, Su T, Yu S, Li T, Zhang T, Zhang J, et al. Active learning accelerates the discovery of high strength and high ductility lead-free solder alloys. Mater Des. 2024;241:112921.</unstructured_citation>
						 <doi>10.1016/j.matdes.2024.112921</doi> 					</citation>
          					<citation key="rk-10.68159/n873145769-2a8a9898-2f96-4460-86b6-c82a9efb77fe">
					  <unstructured_citation>Ng WL, Goh GL, Goh GD, Ten JS, Yeong WY. Progress and opportunities for machine learning in materials and processes of additive manufacturing. Adv Mater. 2024;36(34).</unstructured_citation>
						 <doi>10.1002/adma.202310006</doi> 					</citation>
          					<citation key="rk-10.68159/n873145769-7018c7b7-15c9-47be-a7fa-7173a02b736c">
					  <unstructured_citation>Duan C, Nandy A, Chand Pal S, Yang X, Gao W, Du Y, et al. The rise of generative AI for metal-organic framework design and synthesis. Matter. 2026;9:102748.</unstructured_citation>
						 <doi>10.1016/j.matt.2026.102748</doi> 					</citation>
          					<citation key="rk-10.68159/n873145769-5c5bda55-02be-48c9-adaf-efa468323712">
					  <unstructured_citation>Zhou ZH. Machine learning. Liu S, translator. Singapore: Springer Nature Singapore; 2021.</unstructured_citation>
						 <doi>10.1007/978-981-15-1967-3</doi> 					</citation>
          					<citation key="rk-10.68159/n873145769-e9ed80a6-a97f-4b56-8673-68e1a2b8d417">
					  <unstructured_citation>Ghasemi A, Barisik M. Machine learning for thermal transport prediction in nanoporous materials: Progress, challenges, and opportunities. Nanomaterials (Basel). 2025;15(21):1660.</unstructured_citation>
						 <doi>10.3390/nano15211660</doi> 					</citation>
          					<citation key="rk-10.68159/n873145769-1bd9eee8-daba-4fce-b3fc-ffe68035a19f">
					  <unstructured_citation>Sheng Z, Zhu H, Shao B, He Y, Liu Z, Wang S, et al. Accelerated discovery of energy materials via graph neural network. Inorganics. 2025;13(12):395.</unstructured_citation>
						 <doi>10.3390/inorganics13120395</doi> 					</citation>
          					<citation key="rk-10.68159/n873145769-0f05caff-4282-4743-96ad-c688b1dea134">
					  <unstructured_citation>Osaro E, Colón YJ. Intelligent screening of porous materials: A review of active-learning approaches in MOF research. Chem Phys Rev. 2025;6(4):041307.</unstructured_citation>
						 <doi>10.1063/5.0295283</doi> 					</citation>
          					<citation key="rk-10.68159/n873145769-289b24ab-dbec-4b4c-9070-0d177e286993">
					  <unstructured_citation>Mal S, Seal G, Sen P. MagGen: A graph aided deep generative model for inverse design of stable, permanent magnets [Preprint]. arXiv; 2023.</unstructured_citation>
						 <doi>10.48550/arXiv.2311.13328</doi> 					</citation>
          					<citation key="rk-10.68159/n873145769-55d7f5cb-e3b4-4c3b-aee5-22428b600917">
					  <unstructured_citation>Wang Y, Guo D, Yang T, Qian Q, Liu X, Shi S. A review of topological descriptors for amorphous materials complementing graph neural networks. Comput Mater Today. 2026;10:100053.</unstructured_citation>
						 <doi>10.1016/j.commt.2026.100053</doi> 					</citation>
          				</citation_list>
			</journal_article>
		</journal>
	</body>
</doi_batch>
