<?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-rMzL-1791491021-n504110068</doi_batch_id>
		<timestamp>1791491021</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>Separating What We Know from What We Guess: A Modular Framework for Epistemic vs. Aleatoric Uncertainty in ML Potentials</title>
				</titles>
								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>Ahmed</given_name>
            <surname>El-Kholy</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Nour</given_name>
            <surname>Abdelrahman</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Karim</given_name>
            <surname>Hassan</surname>
					</person_name>
          				</contributors>
								<publication_date>
					<year>2023</year>
				</publication_date>
				<doi_data>
					<doi>10.68159/n504110068</doi>
					<resource>https://iamrp.net/pub/journal/2/article/n504110068</resource>
				</doi_data>
				<citation_list>
          					<citation key="rk-10.68159/n504110068-97b5188e-6154-42b8-847e-2cf5afa2196e">
					  <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/n504110068-848db755-b449-4b06-a5bf-a5cad3e5c304">
					  <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/n504110068-aecb23dd-2259-453d-9c64-7f3e6808247c">
					  <unstructured_citation>Carrete J, Montes-Campos H, Wanzenböck R, Heid E, Madsen GKH. Deep ensembles vs committees for uncertainty estimation in neural-network force fields: Comparison and application to active learning. J Chem Phys. 2023;158(20):204801.</unstructured_citation>
						 <doi>10.1063/5.0146905</doi> 					</citation>
          					<citation key="rk-10.68159/n504110068-9e789ab9-3b85-400d-ac5c-126725d66638">
					  <unstructured_citation>Thaler S, Doehner G, Zavadlav J. Scalable Bayesian uncertainty quantification for neural network potentials: Promise and pitfalls. J Chem Theory Comput. 2023;19(14):4520-32.</unstructured_citation>
						 <doi>10.1021/acs.jctc.2c01267</doi> 					</citation>
          					<citation key="rk-10.68159/n504110068-913ff945-b530-4eca-a462-6bcdd250d8e2">
					  <unstructured_citation>Liu JZ, Paisley J, Kioumourtzoglou MA, Coull BA. Accurate uncertainty estimation and decomposition in ensemble learning. Adv Neural Inf Process Syst. 2019;32:8952-63.</unstructured_citation>
						 <doi>10.5555/3454287.3455090</doi> 					</citation>
          					<citation key="rk-10.68159/n504110068-8febfd2c-5e66-4461-882d-47993a512d37">
					  <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/n504110068-2c94d87b-9d8d-46fb-b1a1-5a715c138d68">
					  <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/n504110068-ad65a784-903e-4132-9e7d-1ef64b95c59f">
					  <unstructured_citation>Bartók AP, Kermode J, Bernstein N, Csányi G. Machine learning a general-purpose interatomic potential for silicon. Phys Rev X. 2018;8(4):041048.</unstructured_citation>
						 <doi>10.1103/PhysRevX.8.041048</doi> 					</citation>
          					<citation key="rk-10.68159/n504110068-57492b79-973d-4dee-a789-f914c544dbc7">
					  <unstructured_citation>Behler J. First principles neural network potentials for reactive simulations of large molecular and condensed systems. Angew Chem Int Ed. 2017;56(42):12828-40.</unstructured_citation>
						 <doi>10.1002/anie.201703114</doi> 					</citation>
          					<citation key="rk-10.68159/n504110068-7a2cdf6f-186e-418f-9f53-5508e426850f">
					  <unstructured_citation>Chmiela S, Sauceda HE, Müller KR, Tkatchenko A. Towards exact molecular dynamics simulations with machine-learned force fields. Nat Commun. 2018;9(1):3887.</unstructured_citation>
						 <doi>10.1038/s41467-018-06169-2</doi> 					</citation>
          					<citation key="rk-10.68159/n504110068-65c2be5e-0ad8-491b-a927-d84fa146cc75">
					  <unstructured_citation>Bartók AP, De S, Poelking C, Bernstein N, Kermode JR, Csányi G, et al. Machine learning unifies the modeling of materials and molecules. Sci Adv. 2017;3(12):e1701816.</unstructured_citation>
						 <doi>10.1126/sciadv.1701816</doi> 					</citation>
          					<citation key="rk-10.68159/n504110068-3786e9c9-b96e-44f7-8544-b2115263bf06">
					  <unstructured_citation>Chen H, Stoddart JF. From molecular to supramolecular electronics. Nat Rev Mater. 2021;6(9):804-28.</unstructured_citation>
						 <doi>10.1038/s41578-021-00302-2</doi> 					</citation>
          					<citation key="rk-10.68159/n504110068-d8da5f01-004b-4551-8925-207e46b09eaa">
					  <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/n504110068-6fc75d61-e697-4b99-957f-649451c20af8">
					  <unstructured_citation>Goodfellow I, Bengio Y, Courville A. Deep learning. Cambridge (MA): MIT Press; 2016.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/n504110068-30107a4e-47f6-47c8-8c5e-498898582ffc">
					  <unstructured_citation>Jinnouchi R, Karsai F, Kresse G. On-the-fly machine learning force field generation: Application to melting points. Phys Rev B. 2019;100(1):014105.</unstructured_citation>
						 <doi>10.1103/PhysRevB.100.014105</doi> 					</citation>
          					<citation key="rk-10.68159/n504110068-967ce1e2-4ecd-4ad9-aa22-3c2df9a18e40">
					  <unstructured_citation>Xie Y, Vandermause J, Sun L, Cepellotti A, Kozinsky B. Bayesian force fields from active learning for simulation of inter-dimensional transformation of stanene. NPJ Comput Mater. 2021;7(1):40.</unstructured_citation>
						 <doi>10.1038/s41524-021-00510-y</doi> 					</citation>
          					<citation key="rk-10.68159/n504110068-4ce5f208-382e-4f07-9fb0-c5322c465649">
					  <unstructured_citation>Kulichenko M, Barros K, Lubbers N, Li YW, Messerly R, Tretiak S, et al. Uncertainty-driven dynamics for active learning of interatomic potentials. Nat Comput Sci. 2023;3(3):230-9.</unstructured_citation>
						 <doi>10.1038/s43588-023-00406-5</doi> 					</citation>
          					<citation key="rk-10.68159/n504110068-7207bb3e-8cb3-4d64-84cb-86f553d4fd79">
					  <unstructured_citation>Hüllermeier E, Waegeman W. Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Mach Learn. 2021;110(3):457-506.</unstructured_citation>
						 <doi>10.1007/s10994-021-05946-3</doi> 					</citation>
          					<citation key="rk-10.68159/n504110068-ed6b6886-5e1b-4b2f-8b73-2b5ed7a5cf52">
					  <unstructured_citation>Wang H, Zhang L, Han J, E W. DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics. Comput Phys Commun. 2018;228:178-84.</unstructured_citation>
						 <doi>10.1016/j.cpc.2018.03.016</doi> 					</citation>
          					<citation key="rk-10.68159/n504110068-19f8cd11-3196-443d-9573-9c26481caf98">
					  <unstructured_citation>Olivier A, Shields MD, Graham-Brady L. Bayesian neural networks for uncertainty quantification in data-driven materials modeling. Comput Methods Appl Mech Eng. 2021;386:114079.</unstructured_citation>
						 <doi>10.1016/j.cma.2021.114079</doi> 					</citation>
          					<citation key="rk-10.68159/n504110068-e2e3cfab-856d-42f5-8d15-e99d10fc4d42">
					  <unstructured_citation>Vazquez-Salazar LI, Boittier ED, Meuwly M. Uncertainty quantification for predictions of atomistic neural networks. Chem Sci. 2022;13(44):13068-84.</unstructured_citation>
						 <doi>10.1039/d2sc04056e</doi> 					</citation>
          					<citation key="rk-10.68159/n504110068-d766e5cf-b4d8-4795-9365-f24fcc891217">
					  <unstructured_citation>Zhu A, Batzner S, Musaelian A, Kozinsky B. Fast uncertainty estimates in deep learning interatomic potentials. J Chem Phys. 2023;158(16):164111.</unstructured_citation>
						 <doi>10.1063/5.0136574</doi> 					</citation>
          					<citation key="rk-10.68159/n504110068-0408dcf2-cb4e-49b0-97c1-0bb1ed6de76b">
					  <unstructured_citation>Kahle L, Zipoli F. Quality of uncertainty estimates from neural network potential ensembles. Phys Rev E. 2022;105(1):015311.</unstructured_citation>
						 <doi>10.1103/PhysRevE.105.015311</doi> 					</citation>
          					<citation key="rk-10.68159/n504110068-e7c438cf-05d0-47bb-9fbe-8def69d8b5b9">
					  <unstructured_citation>Vassaux M, Wan S, Edeling W, Coveney PV. Ensembles are required to handle aleatoric and parametric uncertainty in molecular dynamics simulation. J Chem Theory Comput. 2021;17(8):5187-97.</unstructured_citation>
						 <doi>10.1021/acs.jctc.1c00526</doi> 					</citation>
          					<citation key="rk-10.68159/n504110068-7ffad3a9-0575-4f1a-96e2-280ac66f8b6f">
					  <unstructured_citation>Heid E, McGill CJ, Vermeire FH, Green WH. Characterizing uncertainty in machine learning for chemistry. J Chem Inf Model. 2023;63(13):4012-29.</unstructured_citation>
						 <doi>10.1021/acs.jcim.3c00373</doi> 					</citation>
          					<citation key="rk-10.68159/n504110068-b10f3262-a565-4ad9-91da-c7034e853a7c">
					  <unstructured_citation>Klawohn S, Darby JP, Kermode JR, Csányi G, Caro MA, Bartók AP. Gaussian approximation potentials: Theory, software implementation and application examples. J Chem Phys. 2023;159(17):174108.</unstructured_citation>
						 <doi>10.1063/5.0160898</doi> 					</citation>
          					<citation key="rk-10.68159/n504110068-08037696-4f88-4ed7-88a5-3570a8310526">
					  <unstructured_citation>Deringer VL, Caro MA, Csányi G. Machine learning interatomic potentials as emerging tools for materials science. Adv Mater. 2019;31(46):1902765.</unstructured_citation>
						 <doi>10.1002/adma.201902765</doi> 					</citation>
          					<citation key="rk-10.68159/n504110068-90601f6a-15e4-4c8c-a55a-440a6d53feee">
					  <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/n504110068-02301167-1ad4-4b38-94bf-e0845bfe4748">
					  <unstructured_citation>Zhang L, Han J, Wang H, Saidi WA, Car R, E W. End-to-end symmetry preserving inter-atomic potential energy model for finite and extended systems. Adv Neural Inf Process Syst. 2018;31:4436-46.</unstructured_citation>
						 <doi>10.5555/3327345.3327356</doi> 					</citation>
          				</citation_list>
			</journal_article>
		</journal>
	</body>
</doi_batch>
