<?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-Cd9o-1791488508-d956842056</doi_batch_id>
		<timestamp>1791488508</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>2025</year>
				</publication_date>
				<journal_volume>
					<volume>4</volume>
				</journal_volume>
				<issue>2</issue>
			</journal_issue>
			<journal_article publication_type="full_text">
				<titles>
					<title>Out-of-Distribution Generalization in Materials AI: A Systematic Review of Domain Shift, Robustness, and What Remains Unsolved</title>
				</titles>
								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>Nikolai</given_name>
            <surname>Ivanov</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Sergey</given_name>
            <surname>Volkov</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Elena</given_name>
            <surname>Morozova</surname>
					</person_name>
          				</contributors>
								<publication_date>
					<year>2025</year>
				</publication_date>
				<doi_data>
					<doi>10.68159/d956842056</doi>
					<resource>https://iamrp.net/pub/journal/2/article/d956842056</resource>
				</doi_data>
				<citation_list>
          					<citation key="rk-10.68159/d956842056-89fe2e45-d28d-4896-b3b2-a25f919d4c18">
					  <unstructured_citation>Segal N, Netanyahu A, Greenman KP, Agrawal P, Gómez-Bombarelli R. Known unknowns: Out-of-distribution property prediction in materials and molecules. NPJ Comput Mater. 2025;11(1):345.</unstructured_citation>
						 <doi>10.1038/s41524-025-01808-x</doi> 					</citation>
          					<citation key="rk-10.68159/d956842056-f8410110-4701-4be8-a3dd-ffafd95c5935">
					  <unstructured_citation>Omee SS, Fu N, Dong R, Hu M, Hu J. Structure-based out-of-distribution (OOD) materials property prediction: A benchmark study. NPJ Comput Mater. 2024;10(1):144.</unstructured_citation>
						 <doi>10.1038/s41524-024-01316-4</doi> 					</citation>
          					<citation key="rk-10.68159/d956842056-b3a8be26-fabd-47d5-951b-1afedbffaa17">
					  <unstructured_citation>Li Q, Miklaucic N, Hu J. Out-of-distribution materials property prediction using adversarial learning based fine-tuning [Preprint]. arXiv; 2024. Available from: https://arxiv.org/abs/2408.09297</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/d956842056-4673e978-27c5-4d41-be8d-3468cd0583e4">
					  <unstructured_citation>Li K, Rubungo AN, Lei X, Persaud D, Choudhary K, DeCost B, et al. Probing out-of-distribution generalization in machine learning for materials. Commun Mater. 2025;6(1):9.</unstructured_citation>
						 <doi>10.1038/s43246-024-00731-w</doi> 					</citation>
          					<citation key="rk-10.68159/d956842056-067835ea-5195-4c11-9247-9ae19cf2bdb6">
					  <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>
						 <doi>10.1038/s41524-023-01012-9</doi> 					</citation>
          					<citation key="rk-10.68159/d956842056-40e9864e-26e2-49e1-bb36-a430ffe79fac">
					  <unstructured_citation>Hu J, Liu D, Fu N, Dong R. Realistic material property prediction using domain adaptation based machine learning. Digit Discov. 2024;3(2):300-12.</unstructured_citation>
						 <doi>10.1039/D3DD00162H</doi> 					</citation>
          					<citation key="rk-10.68159/d956842056-fc660226-4fdb-4d9a-8347-4285a9d4ff37">
					  <unstructured_citation>Merchant A, Batzner S, Schoenholz SS, Aykol M, Cheon G, Cubuk ED. Scaling deep learning for materials discovery. Nature. 2023;624(7990):80-5.</unstructured_citation>
						 <doi>10.1038/s41586-023-06735-9</doi> 					</citation>
          					<citation key="rk-10.68159/d956842056-1819a1c2-cf56-4a26-b5a1-dbcf1cf144d6">
					  <unstructured_citation>Noda K, Wakiuchi A, Hayashi Y, Yoshida R. Advancing extrapolative predictions of material properties through learning to learn using extrapolative episodic training. Commun Mater. 2025;6(1):36.</unstructured_citation>
						 <doi>10.1038/s43246-025-00754-x</doi> 					</citation>
          					<citation key="rk-10.68159/d956842056-a29d9a2c-2606-4b32-ada1-c53a88c51779">
					  <unstructured_citation>Hatakeyama-Sato K, Oyaizu K. Generative models for extrapolation prediction in materials informatics. ACS Omega. 2021;6(22):14566-74.</unstructured_citation>
						 <doi>10.1021/acsomega.1c01716</doi> 					</citation>
          					<citation key="rk-10.68159/d956842056-514c2c8b-5ee7-42ba-9abd-928a2676a283">
					  <unstructured_citation>Fu N, Omee SS, Hu J. Physical encoding improves OOD performance in deep learning materials property prediction. Comput Mater Sci. 2025;248:113603.</unstructured_citation>
						 <doi>10.1016/j.commatsci.2024.113603</doi> 					</citation>
          					<citation key="rk-10.68159/d956842056-46bab4da-89b7-445d-a8e2-873633824272">
					  <unstructured_citation>Kauwe SK, Graser J, Murdock R, Sparks TD. Can machine learning find extraordinary materials? Comput Mater Sci. 2020;174:109498.</unstructured_citation>
						 <doi>10.1016/j.commatsci.2019.109498</doi> 					</citation>
          					<citation key="rk-10.68159/d956842056-293fade8-a02d-473c-9c9e-dc9007af1c7c">
					  <unstructured_citation>Muckley ES, Saal JE, Meredig B, Roper CS, Martin JH. Interpretable models for extrapolation in scientific machine learning. Digit Discov. 2023;2(5):1425-35.</unstructured_citation>
						 <doi>10.1039/D3DD00082F</doi> 					</citation>
          					<citation key="rk-10.68159/d956842056-004f5202-0eec-4654-8b9f-646d074154ed">
					  <unstructured_citation>Park H, Zhu R, Huerta EA, Chaudhuri S, Tajkhorshid E, Cooper D. End-to-end AI framework for interpretable prediction of molecular and crystal properties. Mach Learn Sci Technol. 2023;4(2):025036.</unstructured_citation>
						 <doi>10.1088/2632-2153/acd434</doi> 					</citation>
          					<citation key="rk-10.68159/d956842056-0c19fbf3-1742-4120-a203-6ddfafc13448">
					  <unstructured_citation>Li Q, Fu N, Omee SS, Hu J. MD-HIT: Machine learning for material property prediction with dataset redundancy control. NPJ Comput Mater. 2024;10(1):245.</unstructured_citation>
						 <doi>10.1038/s41524-024-01426-z</doi> 					</citation>
          					<citation key="rk-10.68159/d956842056-f430d621-1f1f-4584-b7f5-c244c7e1a757">
					  <unstructured_citation>Madani M, Lacivita V, Shin Y, Tarakanova A. Accelerating materials property prediction via a hybrid Transformer Graph framework that leverages four body interactions. NPJ Comput Mater. 2025;11(1):15.</unstructured_citation>
						 <doi>10.1038/s41524-024-01472-7</doi> 					</citation>
          					<citation key="rk-10.68159/d956842056-2407064a-a480-4558-a724-54cb236ab355">
					  <unstructured_citation>Shimakawa H, Kumada A, Sato M. Extrapolative prediction of small-data molecular property using quantum mechanics-assisted machine learning. NPJ Comput Mater. 2024;10(1):11.</unstructured_citation>
						 <doi>10.1038/s41524-023-01194-2</doi> 					</citation>
          					<citation key="rk-10.68159/d956842056-3f530220-b589-481b-a80e-6be52ea93c71">
					  <unstructured_citation>Ma Y, Xu P, Li M, Ji X, Zhao W, Lu W. The mastery of details in the workflow of materials machine learning. NPJ Comput Mater. 2024;10(1):141.</unstructured_citation>
						 <doi>10.1038/s41524-024-01331-5</doi> 					</citation>
          					<citation key="rk-10.68159/d956842056-31323c35-db5b-47b2-b2b8-27690b6f1498">
					  <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>
						 <doi>10.1038/s41524-024-01468-3</doi> 					</citation>
          					<citation key="rk-10.68159/d956842056-249c79f6-2ec7-42c8-87ef-b22224e63680">
					  <unstructured_citation>Kobayashi W, Otsuka T, Wakabayashi YK, Tei G. Physics-informed Bayesian optimization suitable for extrapolation of materials growth. NPJ Comput Mater. 2025;11(1):36.</unstructured_citation>
						 <doi>10.1038/s41524-025-01522-8</doi> 					</citation>
          					<citation key="rk-10.68159/d956842056-ae2882f7-adab-43aa-ad14-f89db83e54ab">
					  <unstructured_citation>Liao W, Yuan R, Xue X, Wang J, Li J, Lookman T. Unsupervised learning-aided extrapolation for accelerated design of superalloys. NPJ Comput Mater. 2024;10(1):171.</unstructured_citation>
						 <doi>10.1038/s41524-024-01358-8</doi> 					</citation>
          					<citation key="rk-10.68159/d956842056-3def53e5-a4fd-4cfb-af5c-3ff1873cc903">
					  <unstructured_citation>Schultz LE, Wang Y, Jacobs R, Morgan D. A general approach for determining applicability domain of machine learning models. NPJ Comput Mater. 2025;11(1):95.</unstructured_citation>
						 <doi>10.1038/s41524-025-01573-x</doi> 					</citation>
          					<citation key="rk-10.68159/d956842056-a98411d3-96e1-4cad-b11b-7486e7cabd57">
					  <unstructured_citation>Wen X, Liu H, Long W, Wei S, Zhu R. Consistent semantic representation learning for out-of-distribution molecular property prediction. Brief Bioinform. 2025;26(2).</unstructured_citation>
						 <doi>10.1093/bib/bbaf147</doi> 					</citation>
          					<citation key="rk-10.68159/d956842056-24d4e0d6-1131-49b3-ab0b-1f38c5d33a44">
					  <unstructured_citation>Chuah J, Kruger U, Wang G, Yan P, Hahn J. Framework for testing robustness of machine learning-based classifiers. J Pers Med. 2022;12(8):1314.</unstructured_citation>
						 <doi>10.3390/jpm12081314</doi> 					</citation>
          					<citation key="rk-10.68159/d956842056-08dd540f-0ea9-4398-b4f5-b11322e3a544">
					  <unstructured_citation>Panigrahi B, Razavi S, Doig LE, Cordell B, Gupta HV, Liber K. On robustness of the explanatory power of machine learning models: Insights from a new explainable AI approach using sensitivity analysis. Water Resour Res. 2025;61(3).</unstructured_citation>
						 <doi>10.1029/2024WR037398</doi> 					</citation>
          					<citation key="rk-10.68159/d956842056-389e0d3f-7146-46b8-af0c-ab3f674f993f">
					  <unstructured_citation>Zhang H, Zhao Z, Wang C, Zhang X, Chen X. Mitigating domain shift in online process monitoring for material extrusion additive manufacturing via transfer learning. Addit Manuf. 2024;94:104467.</unstructured_citation>
						 <doi>10.1016/j.addma.2024.104467</doi> 					</citation>
          					<citation key="rk-10.68159/d956842056-c2eba6bf-01b3-430d-b84b-36e99342e3bd">
					  <unstructured_citation>Wang H, Li K, Ramsay S, Fehlis Y, Kim E, Hattrick-Simpers J. Evaluating the performance and robustness of LLMs in materials science Q&amp;A and property predictions. Digit Discov. 2025;4(6):1612-24.</unstructured_citation>
						 <doi>10.1039/D5DD00090D</doi> 					</citation>
          					<citation key="rk-10.68159/d956842056-38319820-9b70-4cbf-9bd0-58b8c8d46b97">
					  <unstructured_citation>Zou D, Liu S, Miao S, Fung V, Chang S, Li P. GeSS: Benchmarking geometric deep learning under scientific applications with distribution shifts. In: Proceedings of the 38th Conference on Neural Information Processing Systems; 2024 Dec 10-15; Vancouver, Canada. p. 92499-528.</unstructured_citation>
						 <doi>10.52202/079017-2937</doi> 					</citation>
          					<citation key="rk-10.68159/d956842056-6963c91d-9751-4a17-b108-388efaa18457">
					  <unstructured_citation>Onwuli A, Butler KT, Walsh A. Ionic species representations for materials informatics. APL Mach Learn. 2024;2(3):036112.</unstructured_citation>
						 <doi>10.1063/5.0227009</doi> 					</citation>
          					<citation key="rk-10.68159/d956842056-7350da8c-9703-4961-ad7c-aad7c467a4c7">
					  <unstructured_citation>Yang Y, Zhang Y, Song X, Xu Y. Not all out-of-distribution data are harmful to open-set active learning. In: Advances in Neural Information Processing Systems 36; 2023 Dec 10-16; New Orleans, LA. p. 13802-18.</unstructured_citation>
						 <doi>10.52202/075280-0608</doi> 					</citation>
          					<citation key="rk-10.68159/d956842056-dab7171d-5387-45fd-ae54-601fd2a8941a">
					  <unstructured_citation>Kong D, Pang B, Han T, Wu YN. Molecule design by latent space energy-based modeling and gradual distribution shifting. In: Evans RJ, Shpitser I, editors. Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence; 2023 Jul 31-Aug 4; Pittsburgh, PA. Proceedings of Machine Learning Research. 2023;216:1109-20.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/d956842056-157887a6-1374-42ca-8b59-9f6f13033ae9">
					  <unstructured_citation>Atghaei A, Rahmati M. Domain generalization via geometric adaptation over augmented data. Knowl Based Syst. 2025;309:112765.</unstructured_citation>
						 <doi>10.1016/j.knosys.2024.112765</doi> 					</citation>
          					<citation key="rk-10.68159/d956842056-3af40f43-9965-4be4-8823-72f0e4d31bd2">
					  <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/d956842056-3f21e0d5-acfe-4684-be24-17ab35df88df">
					  <unstructured_citation>Tonguz OK, Taschin F. On the measurement of distribution shift in machine learning systems. IEEE Intell Syst. 2025;40(2):45-54.</unstructured_citation>
						 <doi>10.1109/MIS.2024.3524798</doi> 					</citation>
          					<citation key="rk-10.68159/d956842056-adad5c1a-e6dd-4e9e-8f7b-16ddce4bf203">
					  <unstructured_citation>Zhu R, Tian SI, Ren Z, Li J, Buonassisi T, Hippalgaonkar K. Predicting synthesizability using machine learning on databases of existing inorganic materials. ACS Omega. 2023;8(9):8210-8.</unstructured_citation>
						 <doi>10.1021/acsomega.2c04856</doi> 					</citation>
          					<citation key="rk-10.68159/d956842056-d492f9f9-1690-42c2-aa47-eb8d6121bdd0">
					  <unstructured_citation>Balendran A, Beji C, Bouvier F, Khalifa O, Evgeniou T, Ravaud P, et al. A scoping review of robustness concepts for machine learning in healthcare. NPJ Digit Med. 2025;8(1):38.</unstructured_citation>
						 <doi>10.1038/s41746-024-01420-1</doi> 					</citation>
          				</citation_list>
			</journal_article>
		</journal>
	</body>
</doi_batch>
