<?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-9Sf5-1792542733-z751921562</doi_batch_id>
		<timestamp>1792542733</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>2</issue>
			</journal_issue>
			<journal_article publication_type="full_text">
				<titles>
					<title>When Models Agree for the Wrong Reasons: A Conceptual Analysis of Consensus in Materials AI</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>Pham Quang</given_name>
            <surname>Minh</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Le Thi</given_name>
            <surname>Bich</surname>
					</person_name>
          				</contributors>
								<publication_date>
					<year>2022</year>
				</publication_date>
				<doi_data>
					<doi>10.68159/z751921562</doi>
					<resource>https://iamrp.net/pub/journal/1/article/z751921562</resource>
				</doi_data>
				<citation_list>
          					<citation key="rk-10.68159/z751921562-56f49af6-d45d-4b06-baa8-485865d72232">
					  <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(1):43523.</unstructured_citation>
						 <doi>10.1038/s41598-025-27524-6</doi> 					</citation>
          					<citation key="rk-10.68159/z751921562-7ab9b5ab-b35e-4662-bddc-e8565595e9f2">
					  <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>
						 <doi>10.1021/acsomega.1c03752</doi> 					</citation>
          					<citation key="rk-10.68159/z751921562-ac98205b-38b9-4a29-92d1-a2bc02587ad9">
					  <unstructured_citation>Pyzer-Knapp EO, Pitera JW, Staar PWJ, Takeda S, Laino T, Sanders DP, et al. Accelerating materials discovery using artificial intelligence, high performance computing and robotics. Npj Comput Mater. 2022;8(1):84.</unstructured_citation>
						 <doi>10.1038/s41524-022-00765-z</doi> 					</citation>
          					<citation key="rk-10.68159/z751921562-b934c38b-698b-4477-b139-87ec5e88f628">
					  <unstructured_citation>Rodrigues JF, Florea L, de Oliveira MCF, Diamond D, Oliveira Jr ON. Big data and machine learning for materials science. Discov Mater. 2021;1(1):12.</unstructured_citation>
						 <doi>10.1007/s43939-021-00012-0</doi> 					</citation>
          					<citation key="rk-10.68159/z751921562-b4b03862-e73d-4d9a-970d-62915e2b73ad">
					  <unstructured_citation>Zhou QH, Chen X, Wang JL. Machine learning assisted material discovery: A small data approach. Acc Mater Res. 2021;2(4):239-48.</unstructured_citation>
						 <doi>10.1021/accountsmr.1c00236</doi> 					</citation>
          					<citation key="rk-10.68159/z751921562-4b1eaa33-adf9-4e3e-a3be-b86c7b3c3831">
					  <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):1-23.</unstructured_citation>
						 <doi>10.26599/NR.2025.94908095</doi> 					</citation>
          					<citation key="rk-10.68159/z751921562-b0ee6b8b-34b3-4507-bfc7-c4f81fe72a82">
					  <unstructured_citation>Kobayashi R, Amos RD, Zhang T-Y. Unlocking the future of materials science: Key insights from the dctmd workshop. J Mater Inform. 2025;5(1):50.</unstructured_citation>
						 <doi>10.20517/jmi.2025.44</doi> 					</citation>
          					<citation key="rk-10.68159/z751921562-e594947d-dbf3-4bf9-81ce-9c8eab1bb96d">
					  <unstructured_citation>Karande P, Gallagher B, Han TY-J. A strategic approach to machine learning for material science: How to tackle real-world challenges and avoid pitfalls. Chem Mater. 2022;34(17):7650-65.</unstructured_citation>
						 <doi>10.1021/acs.chemmater.2c01333</doi> 					</citation>
          					<citation key="rk-10.68159/z751921562-a2cc5e70-8a99-4998-812d-6b197cb48505">
					  <unstructured_citation>Axelrod S, Schwalbe-Koda D, Mohapatra S, Damewood J, Greenman KP, Gómez-Bombarelli R. Learning matter: Materials design with machine learning and atomistic simulations. Acc Mater Res. 2022;3(3):343-57.</unstructured_citation>
						 <doi>10.1021/accountsmr.1c00238</doi> 					</citation>
          					<citation key="rk-10.68159/z751921562-ea91e7cb-8f16-4d9a-a5b6-b24c838ea688">
					  <unstructured_citation>Abdelaal A, Tropsha A. Machine learning of material properties: Predictive and interpretable multilinear models. Sci Adv. 2022;8(18):eabm7185.</unstructured_citation>
						 <doi>10.1126/sciadv.abm7185</doi> 					</citation>
          					<citation key="rk-10.68159/z751921562-80642c4d-5585-4d94-875a-9b789d12e881">
					  <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/z751921562-e411d94e-2ac8-4668-8e2c-0e5b250d8d21">
					  <unstructured_citation>Luo X-L, Lyu C-C, Zhang L-W. Physics-informed ensemble learning for robustly extrapolating and revealing fatigue life of composites. Compos Sci Technol. 2025;271:111302.</unstructured_citation>
						 <doi>10.1016/j.compscitech.2025.111302</doi> 					</citation>
          					<citation key="rk-10.68159/z751921562-16fe8a21-4142-4d83-9895-9563552fbd1f">
					  <unstructured_citation>Tran R, Lan J, Shuaibi M, Wood B, Zitnick CL, Ulissi ZW. Materials property prediction with uncertainty quantification: A benchmark study. Appl Phys Rev. 2023;10(2):021409.</unstructured_citation>
						 <doi>10.1063/5.0142830</doi> 					</citation>
          					<citation key="rk-10.68159/z751921562-6e7b8c0a-60fc-409c-acd6-3b7c52a86657">
					  <unstructured_citation>Schmidt J, Shi R, Berri S, Da Silva L, Chen H, Adams S. Distilling ensemble of explanations for weakly-supervised pre-training of image segmentation models. Acta Mater. 2020;197:253-64.</unstructured_citation>
						 <doi>10.1016/j.actamat.2020.07.041</doi> 					</citation>
          					<citation key="rk-10.68159/z751921562-6aab7d39-e818-4e3c-bb8c-0c7b43cbb16c">
					  <unstructured_citation>Palmer JA, Tavazza F, Becker CA. Uncertainty propagation in reduced order models based on crystal plasticity. Sci Adv. 2023;9(25):eadg3265.</unstructured_citation>
						 <doi>10.1126/sciadv.adg3265</doi> 					</citation>
          					<citation key="rk-10.68159/z751921562-b13a700a-ba39-40e8-b961-dc9f572025ac">
					  <unstructured_citation>Zhao Y, Chen X, Feng G. Ensemble learning predicts multiple medications’ adverse drug reactions yet allows a comprehensive pharmacovigilance. Matter. 2021;4(10):3270-88.</unstructured_citation>
						 <doi>10.1016/j.matt.2021.07.028</doi> 					</citation>
          					<citation key="rk-10.68159/z751921562-3d1b8013-7529-4191-b61a-2b39a15f1b9f">
					  <unstructured_citation>Chen L, Batra R, Tran R, Kim C, Ramprasad R. Machine learning models for the prediction of energy, forces, and stresses for molecules. Adv Mater. 2020;32(49):2004380.</unstructured_citation>
						 <doi>10.1002/adma.202004380</doi> 					</citation>
          					<citation key="rk-10.68159/z751921562-301edd40-73b2-4a3f-83ab-57200aa6f821">
					  <unstructured_citation>Frey N, Hase F, Stiaszny B, Sauceda HE, Müller KR, Tkatchenko A. Neural network potentials based on equivariant graph representations for material properties and molecular dynamics simulations. Nat Commun. 2023;14:3562.</unstructured_citation>
						 <doi>10.1038/s41467-023-39217-6</doi> 					</citation>
          					<citation key="rk-10.68159/z751921562-cf5131c9-377a-48e3-9b38-e364d526e7cc">
					  <unstructured_citation>Fung V, Zhang J, Hu G, Ganesh P, Sumpter BG. Inverse design of two-dimensional materials with invertible neural networks. Npj Comput Mater. 2021;7(1):201.</unstructured_citation>
						 <doi>10.1038/s41524-021-00665-8</doi> 					</citation>
          					<citation key="rk-10.68159/z751921562-2c22e7c8-daf2-446f-a3d4-e82343ef0f3f">
					  <unstructured_citation>Zhang J, Seko A, Shitara K, Babu DJ, Hayashi M, Nakayama T, et al. Materials informatics for process and material co-optimization of in-situ cured carbon fiber reinforced polymer composites. Adv Funct Mater. 2020;30(44):2004008.</unstructured_citation>
						 <doi>10.1002/adfm.202004008</doi> 					</citation>
          					<citation key="rk-10.68159/z751921562-32697c8d-2ded-445b-862e-28ea4a880b80">
					  <unstructured_citation>Meredig B, Antoniuk E, Greenman L, Foley B, Agrawal A, Gur I, et al. Autonomous and active training for materials property prediction. Sci Adv. 2021;7(49):eabf7920.</unstructured_citation>
						 <doi>10.1126/sciadv.abf7920</doi> 					</citation>
          					<citation key="rk-10.68159/z751921562-38a92299-6a90-4cf7-ad15-88107a8e9c5f">
					  <unstructured_citation>Frey N, Soklaski R, Axelrod S, Yang S, Elber R, Wiggins J, et al. Neural scaling of deep chemical models. Nat Mach Intell. 2023;5:1297-305.</unstructured_citation>
						 <doi>10.1038/s42256-023-00740-3</doi> 					</citation>
          					<citation key="rk-10.68159/z751921562-21cb4ee9-985f-416f-af57-907ae0e6e745">
					  <unstructured_citation>Dan Y, Zhao Y, Li X, Li S, Hu M, Hu J. Generative adversarial networks (gan) based efficient sampling of chemical composition space for inverse materials design. Npj Comput Mater. 2020;6(1):84.</unstructured_citation>
						 <doi>10.1038/s41524-020-00352-0</doi> 					</citation>
          					<citation key="rk-10.68159/z751921562-292288e7-6924-49f7-a31d-d0f1eafbb4cb">
					  <unstructured_citation>Pilania G, Gubernatis JE, Lookman T. Multi-fidelity machine learning models for accurate bandgap predictions of solids. Acta Mater. 2020;196:552-62.</unstructured_citation>
						 <doi>10.1016/j.actamat.2020.07.003</doi> 					</citation>
          					<citation key="rk-10.68159/z751921562-954decea-6343-4541-8704-548d836cc4a7">
					  <unstructured_citation>Xu H, Liu S, Koh CW, Huang X, Lan Y, Chen J, et al. Machine learning assisted hierarchical computational screening of two-dimensional semiconductors. Npj Comput Mater. 2021;7(1):99.</unstructured_citation>
						 <doi>10.1038/s41524-021-00565-3</doi> 					</citation>
          					<citation key="rk-10.68159/z751921562-93a3a13d-9d40-4bea-89e2-c6f84c02b9cd">
					  <unstructured_citation>Lu S, Zhou Q, Guo Y, Zhang Y, Wu Y, Wang J. Graph neural networks enhanced smart sensor calibrating system for disparate equipment-specific sensing in semiconductor manufacturing. Adv Mater. 2020;32(50):2006611.</unstructured_citation>
						 <doi>10.1002/adma.202006611</doi> 					</citation>
          					<citation key="rk-10.68159/z751921562-3329b1ed-1ed2-4df6-8df0-465bb2078677">
					  <unstructured_citation>Chen C, Zuo Y, Ye W, Li X, Deng Z, Ong SP. A critical review of machine learning of energy materials. Adv Energy Mater. 2020;10(8):1903242.</unstructured_citation>
						 <doi>10.1002/aenm.201903242</doi> 					</citation>
          					<citation key="rk-10.68159/z751921562-33f56821-7e51-4955-b7a5-17b642df693f">
					  <unstructured_citation>Schmidt J, Marques MRG, Botti S, Marques MAL. Recent advances and applications of machine learning in solid-state materials science. Npj Comput Mater. 2019;5(1):83.</unstructured_citation>
						 <doi>10.1038/s41524-019-0221-0</doi> 					</citation>
          					<citation key="rk-10.68159/z751921562-d7316b3d-fa7d-413f-8e50-a9f674c176f5">
					  <unstructured_citation>Zhang Y, Ling C. A strategy to apply machine learning to small datasets in materials science. Npj Comput Mater. 2018;4(1):25.</unstructured_citation>
						 <doi>10.1038/s41524-018-0081-z</doi> 					</citation>
          					<citation key="rk-10.68159/z751921562-5cd10564-98a9-43f4-b8a8-5bd0e47959aa">
					  <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>
						 <doi>10.1038/s41586-018-0337-2</doi> 					</citation>
          					<citation key="rk-10.68159/z751921562-02965343-2775-4c02-a964-a833dca60351">
					  <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/z751921562-1f85ef33-66f0-4c84-8155-637d66911d7e">
					  <unstructured_citation>Oliynyk AO, Adutwum LA, Rudyk BW, Pisavadia H, Law S, Mar A, et al. Classifying crystal structures of binary compounds ab through cluster resolution feature selection and support vector machine analysis. Acta Mater. 2017;126:324-34.</unstructured_citation>
						 <doi>10.1016/j.actamat.2016.12.044</doi> 					</citation>
          					<citation key="rk-10.68159/z751921562-08722981-7e91-42d5-bd83-384c703921e6">
					  <unstructured_citation>Mueller T, Kusne AG, Ramprasad R. Machine learning in materials science: Recent progress and emerging applications. Rev Comput Chem. 2016;29:186-273.</unstructured_citation>
						 <doi>10.1002/9781119148739.ch4</doi> 					</citation>
          					<citation key="rk-10.68159/z751921562-c7ad921b-83af-4dc2-90b5-03b9c7362202">
					  <unstructured_citation>Raccuglia P, Elbert KC, Adler PDF, Falk C, Wenny MB, Mollo A, et al. Machine-learning-assisted materials discovery using failed experiments. Nature. 2016;533(7601):73-6.</unstructured_citation>
						 <doi>10.1038/nature17439</doi> 					</citation>
          				</citation_list>
			</journal_article>
		</journal>
	</body>
</doi_batch>
