<?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-o4nn-1791561620-m640969735</doi_batch_id>
		<timestamp>1791561620</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>Algorithmic Diversity as Scientific Robustness: A Conceptual Framework</title>
				</titles>
								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>Fernando</given_name>
            <surname>Diaz</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Lucia</given_name>
            <surname>Morales</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Diego</given_name>
            <surname>Perez</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Valeria</given_name>
            <surname>Soto</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Martin</given_name>
            <surname>Alvarez</surname>
					</person_name>
          				</contributors>
								<publication_date>
					<year>2026</year>
				</publication_date>
				<doi_data>
					<doi>10.68159/m640969735</doi>
					<resource>https://iamrp.net/pub/journal/1/article/m640969735</resource>
				</doi_data>
				<citation_list>
          					<citation key="rk-10.68159/m640969735-e4ee3618-8422-4a85-85f7-c468364c72c1">
					  <unstructured_citation>Ortega LA, Cabañas R, Masegosa A. Diversity and generalization in neural network ensembles. In: International Conference on Artificial Intelligence and Statistics. PMLR; 2022. p. 11720-43.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/m640969735-583ea7d4-7aee-44cb-8783-d8dde4a35305">
					  <unstructured_citation>Rane N, Choudhary SP, Rane J. Ensemble deep learning and machine learning: Applications, opportunities, challenges, and future directions. Stud Med Health Sci. 2024;1(2):18-41.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/m640969735-b3eb3151-a049-413e-99b2-262070133c8d">
					  <unstructured_citation>Jensen D, LaMacchia B, Topcu U, Wisniewski P. Algorithmic robustness. arXiv preprint arXiv:2311.06275. 2023 Oct 17.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/m640969735-16204ae7-2020-4ff1-b788-73633aff27e8">
					  <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/m640969735-47fc02ec-1626-40df-9c4f-659c66775d1c">
					  <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/m640969735-1c43176d-3a10-4d76-b2fa-61a79d354db4">
					  <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>
											</citation>
          					<citation key="rk-10.68159/m640969735-d023f1c4-8cfe-446d-8c87-09bca5116402">
					  <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/m640969735-e5590860-15fa-49ae-b1eb-cd23acebdd2f">
					  <unstructured_citation>Liu K, Wei Z, Gao W, Dey P, Sluiter MHF, Shuang F. Heterogeneous ensemble enables a universal uncertainty metric for atomistic foundation models. npj Comput Mater. 2026;12:34.</unstructured_citation>
						 <doi>10.1038/s41524-025-01905-x</doi> 					</citation>
          					<citation key="rk-10.68159/m640969735-5f6bdbf2-07df-487a-8f5e-e29be8257338">
					  <unstructured_citation>Vita JA, Samanta A, Zhou F, Lordi V. LTAU-FF: Loss trajectory analysis for uncertainty in atomistic force fields. Mach Learn Sci Technol. 2025;6(1):015048.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/m640969735-39336622-9e65-425a-9ff0-23160b9df687">
					  <unstructured_citation>Jiang X, Sun H, Choudhary K, Zhuang H, Nian Q. Interpretable ensemble learning for materials property prediction with classical interatomic potentials. npj Comput Mater. 2025;11(1):319.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/m640969735-24affea8-66d1-40b1-bcb3-99cbe0147624">
					  <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/m640969735-7d18ea78-a00a-4e39-83d8-94ccf63fecd0">
					  <unstructured_citation>Vinchurkar T, Abdelmaqsoud K, Kitchin JR. Uncertainty quantification in graph neural networks with shallow ensembles. Mach Learn Sci Technol. 2025;6(4):045007.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/m640969735-97b41e7a-cabd-4f40-a1fc-ff0ea7842dea">
					  <unstructured_citation>Rahman CM, Bhandari G, Nasrabadi NM, Romero AH, Gyawali PK. Enhancing material property prediction with ensemble deep graph convolutional networks. Front Mater. 2024;11:1474609.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/m640969735-3d8c68ce-d6af-4c0c-b011-ab016f33c351">
					  <unstructured_citation>Yang LH, Da B, Ding ZJ. Ensemble machine learning methods: Predicting electron stopping powers from a small experimental database. Phys Chem Chem Phys. 2021;23(10):6062-74.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/m640969735-d95ccf74-829d-4339-9b68-a50d4dd0253c">
					  <unstructured_citation>Smyrnov M, Funcke F, Kabliman E. Prediction of material toughness using ensemble learning and data augmentation. Philos Mag Lett. 2024;104(1):2372497.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/m640969735-c562b56f-f8f1-4505-ace9-8aa71987d4bb">
					  <unstructured_citation>Karande P, Gallagher B, Han TY. 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>
											</citation>
          					<citation key="rk-10.68159/m640969735-857ec653-281b-4dc8-8078-9af1e5d1de98">
					  <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/m640969735-be1ada1e-5948-4a1d-bf55-42b3e20695a6">
					  <unstructured_citation>Wang AY, Murdock RJ, Kauwe SK, Oliynyk AO, Gurlo A, Brgoch J, et al. Machine learning for materials scientists: An introductory guide toward best practices. Chem Mater. 2020;32(12):4954-65.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/m640969735-89a44cdb-778d-4844-9c26-fa8da70293a2">
					  <unstructured_citation>Wei J, Chu X, Sun XY, Xu K, Deng HX, Chen J, et al. Machine learning in materials science. InfoMat. 2019;1(3):338-58.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/m640969735-34d1350e-be98-40eb-8db0-5d1c0aaf58bb">
					  <unstructured_citation>Cai J, Chu X, Xu K, Li H, Wei J. Machine learning-driven new material discovery. Nanosc Adv. 2020;2(8):3115-30.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/m640969735-81cbd096-807d-4d70-9244-e0a6c5cd2a1a">
					  <unstructured_citation>Chen CT, Gu GX. Machine learning for composite materials. MRS Commun. 2019;9(2):556-66.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/m640969735-d054f108-2d79-4409-8b9e-8149e9621cbd">
					  <unstructured_citation>Huan TD, Batra R, Chapman J, Krishnan S, Chen L, Ramprasad R. A universal strategy for the creation of machine learning-based atomistic force fields. npj Comput Mater. 2017;3(1):37.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/m640969735-78f57ff9-8207-4ca9-868e-ae0d64f3c844">
					  <unstructured_citation>Baskaran A, Kautz EJ, Chowdhary A, Ma W, Yener B, Lewis DJ. Adoption of image-driven machine learning for microstructure characterization and materials design: A perspective. JOM. 2021;73(11):3639-57.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/m640969735-5bbbda62-5b51-4f3e-9524-5b014d5f39a6">
					  <unstructured_citation>Alipour M, Harris DK. Increasing the robustness of material-specific deep learning models for crack detection across different materials. Eng Struct. 2020;206:110157.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/m640969735-e228e87e-e80f-4ca7-bd7d-f2609ebe99ef">
					  <unstructured_citation>Xiong J, Zhang T, Shi S. Machine learning of mechanical properties of steels. Sci China Technol Sci. 2020;63(7):1247-55.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/m640969735-eb36f5a9-21f3-45ba-a4f2-7aaa252e1abf">
					  <unstructured_citation>Caggiano A, Zhang J, Alfieri V, Caiazzo F, Gao R, Teti R. Machine learning-based image processing for on-line defect recognition in additive manufacturing. CIRP Ann. 2019;68(1):451-4.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/m640969735-0a416b3b-2cac-40b4-905a-f777129f282f">
					  <unstructured_citation>Gobert C, Reutzel EW, Petrich J, Nassar AR, Phoha S. Application of supervised machine learning for defect detection during metallic powder bed fusion additive manufacturing using high resolution imaging. Addit Manuf. 2018;21:517-28.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/m640969735-94736218-1615-4f35-96e8-03311aea1fe8">
					  <unstructured_citation>Borboudakis G, Stergiannakos T, Frysali M, Klontzas E, Tsamardinos I, Froudakis GE. Chemically intuited, large-scale screening of MOFs by machine learning techniques. npj Comput Mater. 2017;3(1):40.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/m640969735-484aff36-c738-4d5e-a04b-aed6b5b31ef2">
					  <unstructured_citation>Stein HS, Guevarra D, Newhouse PF, Soedarmadji E, Gregoire JM. Machine learning of optical properties of materials–predicting spectra from images and images from spectra. Chem Sci. 2019;10(1):47-55.</unstructured_citation>
											</citation>
          				</citation_list>
			</journal_article>
		</journal>
	</body>
</doi_batch>
