<?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-Pp1X-1791492111-z393654920</doi_batch_id>
		<timestamp>1791492111</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>2023</year>
				</publication_date>
				<journal_volume>
					<volume>2</volume>
				</journal_volume>
				<issue>1</issue>
			</journal_issue>
			<journal_article publication_type="full_text">
				<titles>
					<title>Redefining “Out-of-Distribution” for Crystalline Materials Graphs: A Boundary for Domain Shift Detection</title>
				</titles>
								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>Bruno</given_name>
            <surname>Martins</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Lucas</given_name>
            <surname>Pereira</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Renata</given_name>
            <surname>Azevedo</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Pedro</given_name>
            <surname>Costa</surname>
					</person_name>
          				</contributors>
								<publication_date>
					<year>2023</year>
				</publication_date>
				<doi_data>
					<doi>10.68159/z393654920</doi>
					<resource>https://iamrp.net/pub/journal/2/article/z393654920</resource>
				</doi_data>
				<citation_list>
          					<citation key="rk-10.68159/z393654920-42720b2f-44cd-4ddc-a2d0-337596c1e96c">
					  <unstructured_citation>Wang Y, Liu Y, Song S, Yang Z, Qi X, Wang K, et al. Accelerating the discovery of insensitive high-energy-density materials by a materials genome approach. Nat Commun. 2018;9(1):2444.</unstructured_citation>
						 <doi>10.1038/s41467-018-04897-z</doi> 					</citation>
          					<citation key="rk-10.68159/z393654920-b351d4b7-0896-466e-b751-77bcf0f75ebd">
					  <unstructured_citation>Schütt KT, Sauceda HE, Kindermans PJ, Tkatchenko A, Müller KR. SchNet: A deep learning architecture for molecules and materials. J Chem Phys. 2018;148(24):241722.</unstructured_citation>
						 <doi>10.1063/1.5019779</doi> 					</citation>
          					<citation key="rk-10.68159/z393654920-d403496f-9f87-4a2b-a0ed-3af85270373b">
					  <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/z393654920-fe415bac-1ab4-46bc-bdde-8bbb072b65b0">
					  <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/z393654920-34298d06-7a8f-4b06-972a-19d5ae2d95a0">
					  <unstructured_citation>Zhou Q, Lu S, Wu Y, Wang J. Property-oriented material design based on a data-driven machine learning technique. J Phys Chem Lett. 2020;11(10):3920-7.</unstructured_citation>
						 <doi>10.1021/acs.jpclett.0c00665</doi> 					</citation>
          					<citation key="rk-10.68159/z393654920-7ab348eb-8217-4b7d-9f72-3b742e2ebd72">
					  <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/z393654920-1cb0e3f5-2d56-42db-87c9-175dd921ab47">
					  <unstructured_citation>Yamada H, Liu C, Wu S, Koyama Y, Ju S, Shiomi J, et al. Predicting materials properties with little data using shotgun transfer learning. ACS Cent Sci. 2019;5(10):1717-30.</unstructured_citation>
						 <doi>10.1021/acscentsci.9b00804</doi> 					</citation>
          					<citation key="rk-10.68159/z393654920-a4b9ac1d-1e16-414d-93f9-3165425a40a3">
					  <unstructured_citation>Jha D, Choudhary K, Tavazza F, Liao WK, Choudhary A, Campbell C, et al. Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning. Nat Commun. 2019;10(1):5316.</unstructured_citation>
						 <doi>10.1038/s41467-019-13297-w</doi> 					</citation>
          					<citation key="rk-10.68159/z393654920-aef75dce-3d44-4016-9c7d-e4dc169a93d0">
					  <unstructured_citation>Gupta V, Choudhary K, Tavazza F, Campbell C, Liao WK, Choudhary A, et al. Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data. Nat Commun. 2021;12(1):6595.</unstructured_citation>
						 <doi>10.1038/s41467-021-26921-5</doi> 					</citation>
          					<citation key="rk-10.68159/z393654920-91ae4804-e6c5-4e48-9ce7-a80018eb576a">
					  <unstructured_citation>Lee J, Asahi R. Transfer learning for materials informatics using crystal graph convolutional neural network. Comput Mater Sci. 2021;190:110314.</unstructured_citation>
						 <doi>10.1016/j.commatsci.2021.110314</doi> 					</citation>
          					<citation key="rk-10.68159/z393654920-c0377172-efe4-49ea-a01f-a98f6a90bf6e">
					  <unstructured_citation>Magar R, Wang Y, Barati Farimani A. Crystal twins: Self-supervised learning for crystalline material property prediction. NPJ Comput Mater. 2022;8(1):231.</unstructured_citation>
						 <doi>10.1038/s41524-022-00921-5</doi> 					</citation>
          					<citation key="rk-10.68159/z393654920-4bcc11f5-3309-43e2-bdff-2d1ec2f0bfa1">
					  <unstructured_citation>Liu J, Shen Z, He Y, Zhang X, Xu R, Yu H, et al. Towards out-of-distribution generalization: A survey. arXiv:2108.13624 [Preprint]. 2021.</unstructured_citation>
						 <doi>10.48550/arXiv.2108.13624</doi> 					</citation>
          					<citation key="rk-10.68159/z393654920-452a0f8a-7435-4b73-ac1e-1ca0a2a4656d">
					  <unstructured_citation>Sankaranarayanan S, Balaji Y, Jain A, Lim SN, Chellappa R. Learning from synthetic data: Addressing domain shift for semantic segmentation. In: 2018 IEEE/CVF conference on computer vision and pattern recognition; 2018 Jun 18-22; Salt Lake City, UT. Los Alamitos (CA): IEEE Computer Society; 2018. p. 3752-61.</unstructured_citation>
						 <doi>10.1109/CVPR.2018.00395</doi> 					</citation>
          					<citation key="rk-10.68159/z393654920-56ddde5e-6971-458d-8cb6-7fee5e3d2661">
					  <unstructured_citation>Dai M, Demirel MF, Liang Y, Hu JM. Graph neural networks for an accurate and interpretable prediction of the properties of polycrystalline materials. NPJ Comput Mater. 2021;7(1):103.</unstructured_citation>
						 <doi>10.1038/s41524-021-00574-w</doi> 					</citation>
          					<citation key="rk-10.68159/z393654920-af309f82-4492-45f2-a921-30d98d14a3ec">
					  <unstructured_citation>Tian L, Fan Y, Li L, Mousseau N. Identifying flow defects in amorphous alloys using machine learning outlier detection methods. Scr Mater. 2020;186:185-9.</unstructured_citation>
						 <doi>10.1016/j.scriptamat.2020.05.038</doi> 					</citation>
          					<citation key="rk-10.68159/z393654920-96449900-6b76-4af2-8faa-1a0ea00c6cd3">
					  <unstructured_citation>Guo Y, Kalinin SV, Cai H, Xiao K, Krylyuk S, Davydov AV, et al. Defect detection in atomic-resolution images via unsupervised learning with translational invariance. NPJ Comput Mater. 2021;7(1):180.</unstructured_citation>
						 <doi>10.1038/s41524-021-00642-1</doi> 					</citation>
          					<citation key="rk-10.68159/z393654920-96749d81-ab3d-4efa-817b-9cf16d6c90a7">
					  <unstructured_citation>Yang Z, Buehler MJ. Linking atomic structural defects to mesoscale properties in crystalline solids using graph neural networks. NPJ Comput Mater. 2022;8(1):198.</unstructured_citation>
						 <doi>10.1038/s41524-022-00879-4</doi> 					</citation>
          					<citation key="rk-10.68159/z393654920-1dff6f68-0b5e-4d6b-81a8-53e3087ba9e1">
					  <unstructured_citation>Zuo Y, Chen C, Li X, Deng Z, Chen Y, Behler J, et al. Performance and cost assessment of machine learning interatomic potentials. J Phys Chem A. 2020;124(4):731-45.</unstructured_citation>
						 <doi>10.1021/acs.jpca.9b08723</doi> 					</citation>
          					<citation key="rk-10.68159/z393654920-efa49233-49fb-454e-ad7e-d27d1062af62">
					  <unstructured_citation>Zhang L, Han J, Wang H, Car R, E W. Deep potential molecular dynamics: A scalable model with the accuracy of quantum mechanics. Phys Rev Lett. 2018;120(14):143001.</unstructured_citation>
						 <doi>10.1103/PhysRevLett.120.143001</doi> 					</citation>
          					<citation key="rk-10.68159/z393654920-ac78af3f-3e74-4ef0-abdc-902c53fe15c0">
					  <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/z393654920-ce5a97b5-8a97-4225-8dbe-647f1487dd64">
					  <unstructured_citation>Yan K, Liu Y, Lin Y, Ji S. Periodic graph transformers for crystal material property prediction. In: Advances in neural information processing systems 35; 2022 Nov 28-Dec 9; New Orleans, LA. Neural Information Processing Systems Foundation, Inc.; 2022. p. 15066-80.</unstructured_citation>
						 <doi>10.52202/068431-1096</doi> 					</citation>
          					<citation key="rk-10.68159/z393654920-a0454b77-c185-4d95-badf-1916c2233baf">
					  <unstructured_citation>Sutton C, Boley M, Ghiringhelli LM, Rupp M, Vreeken J, Scheffler M. Identifying domains of applicability of machine learning models for materials science. Nat Commun. 2020;11(1):4428.</unstructured_citation>
						 <doi>10.1038/s41467-020-17112-9</doi> 					</citation>
          					<citation key="rk-10.68159/z393654920-28489f5a-e16c-474e-ab09-c3e82fbd8021">
					  <unstructured_citation>Das K, Samanta B, Goyal P, Lee SC, Bhattacharjee S, Ganguly N. CrysXPP: An explainable property predictor for crystalline materials. NPJ Comput Mater. 2022;8(1):43.</unstructured_citation>
						 <doi>10.1038/s41524-022-00716-8</doi> 					</citation>
          					<citation key="rk-10.68159/z393654920-0733f478-590e-4aec-9606-19d53258ea25">
					  <unstructured_citation>Cheng J, Zhang C, Dong L. A geometric-information-enhanced crystal graph network for predicting properties of materials. Commun Mater. 2021;2(1):92.</unstructured_citation>
						 <doi>10.1038/s43246-021-00194-3</doi> 					</citation>
          					<citation key="rk-10.68159/z393654920-9f96dfaa-c0fa-4bb1-90b9-588a9afa28f7">
					  <unstructured_citation>Zhang B, Zhou M, Wu J, Gao F. Predicting the materials properties using a 3D graph neural network with invariant representation. IEEE Access. 2022;10:62440-9.</unstructured_citation>
						 <doi>10.1109/ACCESS.2022.3181750</doi> 					</citation>
          					<citation key="rk-10.68159/z393654920-c99dfe06-0613-444d-ba09-683b94200ad0">
					  <unstructured_citation>Louis SY, Zhao Y, Nasiri A, Wang X, Song Y, Liu F, et al. Graph convolutional neural networks with global attention for improved materials property prediction. Phys Chem Chem Phys. 2020;22(32):18141-8.</unstructured_citation>
						 <doi>10.1039/D0CP01474E</doi> 					</citation>
          					<citation key="rk-10.68159/z393654920-b1ccd962-15e9-4cde-925d-9e9d2e51e410">
					  <unstructured_citation>Chung HW, Freitas R, Cheon G, Reed EJ. Data-centric framework for crystal structure identification in atomistic simulations using machine learning. Phys Rev Mater. 2022;6(4):043801.</unstructured_citation>
						 <doi>10.1103/PhysRevMaterials.6.043801</doi> 					</citation>
          					<citation key="rk-10.68159/z393654920-f71ef77f-06b1-4dfa-a4fb-ef6fb4b23175">
					  <unstructured_citation>Banko L, Maffettone PM, Naujoks D, Olds D, Ludwig A. Deep learning for visualization and novelty detection in large X-ray diffraction datasets. NPJ Comput Mater. 2021;7(1):104.</unstructured_citation>
						 <doi>10.1038/s41524-021-00575-9</doi> 					</citation>
          					<citation key="rk-10.68159/z393654920-c7076bce-0769-4a08-9bbe-4f3630ccf901">
					  <unstructured_citation>Cohn R, Holm E. Unsupervised machine learning via transfer learning and k-means clustering to classify materials image data. Integr Mater Manuf Innov. 2021;10(2):231-44.</unstructured_citation>
						 <doi>10.1007/s40192-021-00205-8</doi> 					</citation>
          				</citation_list>
			</journal_article>
		</journal>
	</body>
</doi_batch>
