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			<depositor_name>Institute for Advanced Materials Research Press</depositor_name>
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				<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>
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				<publication_date>
					<year>2024</year>
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					<volume>3</volume>
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				<issue>1</issue>
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				<titles>
					<title>Separating Epistemic from Aleatoric Uncertainty in Materials Graph Networks for High-Risk Predictions</title>
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          					<person_name sequence="first" contributor_role="author">
            <given_name>Chinedu</given_name>
            <surname>Okafor</surname>
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            <given_name>Amina</given_name>
            <surname>Bello</surname>
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								<publication_date>
					<year>2024</year>
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          					<citation key="rk-10.68159/i271461051-19097bc5-5be9-481f-be9a-2ed214ffdaad">
					  <unstructured_citation>Kendall A, Gal Y. What uncertainties do we need in Bayesian deep learning for computer vision? Adv Neural Inf Process Syst. 2017;30:5574-84.</unstructured_citation>
						 <doi>10.5555/3295222.3295309</doi> 					</citation>
          					<citation key="rk-10.68159/i271461051-8706bce2-ac0c-4f3a-a6f3-1b142fb474fa">
					  <unstructured_citation>Deringer VL, Bartók AP, Bernstein N, Wilkins DM, Ceriotti M, Csányi G. Gaussian process regression for materials and molecules. Chem Rev. 2021;121(16):10073-141.</unstructured_citation>
						 <doi>10.1021/acs.chemrev.1c00022</doi> 					</citation>
          					<citation key="rk-10.68159/i271461051-5fb830b6-0b2f-4357-99ba-67a7f40b0e17">
					  <unstructured_citation>Burton S, Herd B. Addressing uncertainty in the safety assurance of machine-learning. Front Comput Sci. 2023;5:1132580.</unstructured_citation>
						 <doi>10.3389/fcomp.2023.1132580</doi> 					</citation>
          					<citation key="rk-10.68159/i271461051-5c7b2f6a-0200-4aa1-919b-ffe8d135a129">
					  <unstructured_citation>Batzner S, Musaelian A, Sun L, Geiger M, Mailoa JP, Kornbluth M, et al. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. Nat Commun. 2022;13(1):2453.</unstructured_citation>
						 <doi>10.1038/s41467-022-29939-5</doi> 					</citation>
          					<citation key="rk-10.68159/i271461051-21f7c9cc-f582-4606-b1a8-deec0f6f79d5">
					  <unstructured_citation>Corso G, Stärk H, Jegelka S, Jaakkola T, Barzilay R. Graph neural networks. Nat Rev Methods Primers. 2024;4(1):17.</unstructured_citation>
						 <doi>10.1038/s43586-024-00294-7</doi> 					</citation>
          					<citation key="rk-10.68159/i271461051-b2b44376-33ad-4c9b-a032-d63a091a62c9">
					  <unstructured_citation>Jiang S, Qin S, Van Lehn RC, Balaprakash P, Zavala VM. Uncertainty quantification for molecular property predictions with graph neural architecture search. Digit Discov. 2024;3(8):1534-53.</unstructured_citation>
						 <doi>10.1039/D4DD00088A</doi> 					</citation>
          					<citation key="rk-10.68159/i271461051-50c0a191-3da5-4ebf-a66a-2f12a36f606a">
					  <unstructured_citation>Munikoti S, Agarwal D, Das L, Natarajan B. A general framework for quantifying aleatoric and epistemic uncertainty in graph neural networks. Neurocomputing. 2023;521:1-10.</unstructured_citation>
						 <doi>10.1016/j.neucom.2022.11.049</doi> 					</citation>
          					<citation key="rk-10.68159/i271461051-c4d193e2-eab6-4a71-aa79-2982cc598f6d">
					  <unstructured_citation>Gruich CJ, Madhavan V, Wang Y, Goldsmith BR. Clarifying trust of materials property predictions using neural networks with distribution-specific uncertainty quantification. Mach Learn Sci Technol. 2023;4(2):025019.</unstructured_citation>
						 <doi>10.1088/2632-2153/accace</doi> 					</citation>
          					<citation key="rk-10.68159/i271461051-a88b8bc7-4d4a-49ab-9975-13f94aa4993c">
					  <unstructured_citation>Musielewicz J, Lan J, Uyttendaele M, Kitchin JR. Improved uncertainty estimation of graph neural network potentials using engineered latent space distances. J Phys Chem C. 2024;128(49):20799-810.</unstructured_citation>
						 <doi>10.1021/acs.jpcc.4c04972</doi> 					</citation>
          					<citation key="rk-10.68159/i271461051-60af83fc-5eed-46f0-b6fa-42d3c7740c81">
					  <unstructured_citation>Varivoda D, Dong R, Omee SS, Hu J. Materials property prediction with uncertainty quantification: A benchmark study. Appl Phys Rev. 2023;10(2):021409.</unstructured_citation>
						 <doi>10.1063/5.0133528</doi> 					</citation>
          					<citation key="rk-10.68159/i271461051-010201b7-43bd-49f7-a157-eb925ab5c056">
					  <unstructured_citation>Lakshminarayanan B, Pritzel A, Blundell C. Simple and scalable predictive uncertainty estimation using deep ensembles. Adv Neural Inf Process Syst. 2017;30:6402-13.</unstructured_citation>
						 <doi>10.5555/3295222.3295387</doi> 					</citation>
          					<citation key="rk-10.68159/i271461051-4d242b7e-23ff-4243-a561-0cc7ba26c8da">
					  <unstructured_citation>Kwon Y, Lee D, Choi YS, Kang S. Uncertainty-aware prediction of chemical reaction yields with graph neural networks. J Cheminform. 2022;14(1):2.</unstructured_citation>
						 <doi>10.1186/s13321-021-00579-z</doi> 					</citation>
          					<citation key="rk-10.68159/i271461051-bbc3dcde-3060-48ac-ad6a-d96f8c64fbd8">
					  <unstructured_citation>Wang S, Yue H, Yuan X. Accelerating polymer discovery with uncertainty-guided PGCNN: Explainable AI for predicting properties and mechanistic insights. J Chem Inf Model. 2024;64(14):5500-9.</unstructured_citation>
						 <doi>10.1021/acs.jcim.4c00555</doi> 					</citation>
          					<citation key="rk-10.68159/i271461051-0bcff310-ceb8-4d4e-ad3f-f8e722cd8d7b">
					  <unstructured_citation>Wang F, Liu Y, Liu K, Wang Y, Medya S, Yu PS. Uncertainty in graph neural networks: A survey. arXiv [Preprint]. 2024. arXiv:2403.07185</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/i271461051-45e6bf54-17aa-48c9-9b53-bb84b0bf8242">
					  <unstructured_citation>Ying R, Bourgeois D, You J, Zitnik M, Leskovec J. GNNExplainer: Generating explanations for graph neural networks. Adv Neural Inf Process Syst. 2019;32:9240-51.</unstructured_citation>
						 <doi>10.5555/3454287.3455116</doi> 					</citation>
          					<citation key="rk-10.68159/i271461051-20d62437-b07b-424d-92c3-f8a044a81c08">
					  <unstructured_citation>Soleimany AP, Amini A, Goldman S, Rus D, Bhatia SN, Coley CW. Evidential deep learning for guided molecular property prediction and discovery. ACS Cent Sci. 2021;7(8):1356-67.</unstructured_citation>
						 <doi>10.1021/acscentsci.1c00546</doi> 					</citation>
          					<citation key="rk-10.68159/i271461051-b18d45ce-af5a-42ff-8b94-22f58d3560d5">
					  <unstructured_citation>Soize C. Uncertainty quantification: An accelerated course with advanced applications in computational engineering. Cham: Springer International Publishing; 2017. xxii, 329 p.</unstructured_citation>
						 <doi>10.1007/978-3-319-54339-0</doi> 					</citation>
          					<citation key="rk-10.68159/i271461051-63a9b490-742d-4730-abdd-37555b376618">
					  <unstructured_citation>Dawood T, Chen C, Sidhu BS, Ruijsink B, Gould J, Porter B, et al. Uncertainty aware training to improve deep learning model calibration for classification of cardiac MR images. Med Image Anal. 2023;88:102861.</unstructured_citation>
						 <doi>10.1016/j.media.2023.102861</doi> 					</citation>
          					<citation key="rk-10.68159/i271461051-22326804-e7fe-4f5b-9676-03cecd68fca0">
					  <unstructured_citation>Fuchsgruber D, Wollschläger T, Günnemann S. Energy-based epistemic uncertainty for graph neural networks. Adv Neural Inf Process Syst. 2024;37:34378-428.</unstructured_citation>
						 <doi>10.52202/079017-1084</doi> 					</citation>
          					<citation key="rk-10.68159/i271461051-1f22aa94-05b1-4685-9599-568beaa91956">
					  <unstructured_citation>Busk J, Schmidt MN, Winther O, Vegge T, Jørgensen PB. Graph neural network interatomic potential ensembles with calibrated aleatoric and epistemic uncertainty on energy and forces. Phys Chem Chem Phys. 2023;25(37):25828-37.</unstructured_citation>
						 <doi>10.1039/D3CP02143B</doi> 					</citation>
          					<citation key="rk-10.68159/i271461051-d539846d-5a72-4c06-b9ea-2521329dfcc9">
					  <unstructured_citation>Wu L, Cui P, Pei J, Zhao L, Guo X. Graph neural networks: Foundation, frontiers and applications. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York: Association for Computing Machinery; 2022. p. 4840-1.</unstructured_citation>
						 <doi>10.1145/3534678.3542609</doi> 					</citation>
          					<citation key="rk-10.68159/i271461051-2d2c8eb2-1093-4f1f-94ad-4a6106edcf35">
					  <unstructured_citation>Li Y, Gal Y. Dropout inference in Bayesian neural networks with alpha-divergences. In: Proceedings of the 34th International Conference on Machine Learning. Proc Mach Learn Res. 2017;70:2052-61.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/i271461051-e7e59761-410e-4ca8-852d-c10e10fc7913">
					  <unstructured_citation>Stadler M, Charpentier B, Geisler S, Zügner D, Günnemann S. Graph posterior network: Bayesian predictive uncertainty for node classification. Adv Neural Inf Process Syst. 2021;34:18033-48.</unstructured_citation>
						 <doi>10.5555/3540261.3541641</doi> 					</citation>
          					<citation key="rk-10.68159/i271461051-1f1b4b79-71a9-49ba-8e30-6ce4935b68be">
					  <unstructured_citation>Alzate-Mejía N, Santos-Boada G, de Almeida-Amazonas JR. Decision-making under uncertainty for the deployment of future hyperconnected networks: A survey. Sensors (Basel). 2021;21(11):3791.</unstructured_citation>
						 <doi>10.3390/s21113791</doi> 					</citation>
          					<citation key="rk-10.68159/i271461051-b06ea87e-c896-499f-a838-0f7e238f5b29">
					  <unstructured_citation>Stocker S, Gasteiger J, Becker F, Günnemann S, Margraf JT. How robust are modern graph neural network potentials in long and hot molecular dynamics simulations? Mach Learn Sci Technol. 2022;3(4):045010.</unstructured_citation>
						 <doi>10.1088/2632-2153/ac9955</doi> 					</citation>
          					<citation key="rk-10.68159/i271461051-82b8d399-26f1-45a5-b57a-61469b6e919f">
					  <unstructured_citation>Zhao X, Chen F, Hu S, Cho JH. Uncertainty aware semi-supervised learning on graph data. Adv Neural Inf Process Syst. 2020;33:12827-36.</unstructured_citation>
						 <doi>10.5555/3495724.3496800</doi> 					</citation>
          					<citation key="rk-10.68159/i271461051-80523ea8-9536-477e-a40a-4464c653cf40">
					  <unstructured_citation>Zhao X, Chen F, Hu S, Cho JH. Uncertainty-aware prediction for graph neural networks. OpenReview [Preprint]. 2019.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/i271461051-ff2eba60-c74c-4e0a-b079-0332865da92d">
					  <unstructured_citation>Wu Z, Pan S, Chen F, Long G, Zhang C, Yu PS. A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Syst. 2021;32(1):4-24.</unstructured_citation>
						 <doi>10.1109/TNNLS.2020.2978386</doi> 					</citation>
          					<citation key="rk-10.68159/i271461051-c327afa2-c8d7-462b-bd15-39b63fb55798">
					  <unstructured_citation>Jiang J, Ling C, Li H, Bai G, Zhao X, Zhao L. Quantifying uncertainty in graph neural network explanations. Front Big Data. 2024;7:1392662.</unstructured_citation>
						 <doi>10.3389/fdata.2024.1392662</doi> 					</citation>
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