<?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-y6ni-1793528139-j862290346</doi_batch_id>
		<timestamp>1793528139</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>2022</year>
				</publication_date>
				<journal_volume>
					<volume>1</volume>
				</journal_volume>
				<issue>2</issue>
			</journal_issue>
			<journal_article publication_type="full_text">
				<titles>
					<title>Default Assumptions as Hidden Parameters: A Theory of Implicit Priors in Materials AI</title>
				</titles>
								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>Ahmed</given_name>
            <surname>Mansour</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Omar</given_name>
            <surname>Saeed</surname>
					</person_name>
          				</contributors>
								<publication_date>
					<year>2022</year>
				</publication_date>
				<doi_data>
					<doi>10.68159/j862290346</doi>
					<resource>https://iamrp.net/pub/journal/1/article/j862290346</resource>
				</doi_data>
				<citation_list>
          					<citation key="rk-10.68159/j862290346-79fc184e-f85d-4289-8055-a94314046fd0">
					  <unstructured_citation>Poggio T, Banburski A, Liao Q. Theoretical issues in deep networks. Proc Natl Acad Sci U S A. 2020;117(48):30039-45.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j862290346-4d1ac35d-1a0e-4904-aa18-372a2c4e9705">
					  <unstructured_citation>Gondal MW, Wuthrich M, Miladinovic D, Locatello F, Breidt M, Volchkov V, et al. On the transfer of inductive bias from simulation to the real world: A new disentanglement dataset. NeurIPS. 2019;32.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j862290346-c2be57c0-8fea-433f-ac41-4ab895b44c28">
					  <unstructured_citation>Zhang C, Bengio S, Hardt M, Recht B, Vinyals O. Understanding deep learning (still) requires rethinking generalization. Commun ACM. 2021;64(3):107-15.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j862290346-6bf4b9a9-9b0c-4dd0-b3f3-bcd6751cf8b0">
					  <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>
											</citation>
          					<citation key="rk-10.68159/j862290346-2a9ea546-4aa3-4561-809f-5cdad766bd5b">
					  <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/j862290346-46e670a7-5769-4dff-8fb2-ddd178b4b984">
					  <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/j862290346-a3bbf705-08bd-4c1a-839c-b77f8b403400">
					  <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/j862290346-09a65dd5-36a1-4d61-be76-f9bfdc2dd0fc">
					  <unstructured_citation>Wang F, Bian Y, Wang H, Lyu M, Pedrini G, Osten W, et al. Phase imaging with an untrained neural network. Light Sci Appl. 2020;9(1):77.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j862290346-7dbf4805-395d-41d6-8038-f46b13b3230c">
					  <unstructured_citation>Ma C, Li Y, Hernández-Lobato JM. Variational implicit processes. In: International Conference on Machine Learning; 2019. p. 4222-33.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j862290346-43979378-86e9-4c06-8bfb-ca908a3c1118">
					  <unstructured_citation>De Breuck PP, Hautier G, Rignanese GM. Materials property prediction for limited datasets enabled by feature selection and joint learning with modnet. npj Comput Mater. 2021;7(1):83.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j862290346-85876362-ccbc-466e-9ee9-4d98ec82679d">
					  <unstructured_citation>Qayyum A, Ilahi I, Shamshad F, Boussaid F, Bennamoun M, Qadir J. Untrained neural network priors for inverse imaging problems: A survey. IEEE Trans Pattern Anal Mach Intell. 2022;45(5):6511-36.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j862290346-a7ba6e1c-4e60-4ba4-b005-95df39284c19">
					  <unstructured_citation>Borisov V, Leemann T, Seßler K, Haug J, Pawelczyk M, Kasneci G. Deep neural networks and tabular data: A survey. IEEE Trans Neural Netw Learn Syst. 2022;35(6):7499-519.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j862290346-c1f11a66-1207-4cdb-bb30-7ab4a741a5fb">
					  <unstructured_citation>Neyshabur B. Implicit regularization in deep learning. arXiv preprint arXiv:1709.01953; 2017.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j862290346-c2251fa0-cded-4877-9452-59bb03db1784">
					  <unstructured_citation>Neyshabur B, Bhojanapalli S, McAllester D, Srebro N. Exploring generalization in deep learning. NeurIPS. 2017;30.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j862290346-b6322c9e-c773-406a-8771-19b139b4abdd">
					  <unstructured_citation>Liu S, Okatani T. Symmetry-aware neural architecture for embodied visual exploration. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2022. p. 17221-30.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j862290346-ff833889-3829-4882-8a8f-643769c3432a">
					  <unstructured_citation>Mathew A, Amudha P, Sivakumari S. Deep learning techniques: An overview. In: International Conference on Advanced Machine Learning Technologies and Applications. Singapore: Springer; 2020. p. 599-608.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j862290346-57fdca04-f5fb-4189-9a39-3fd98f6611d0">
					  <unstructured_citation>Canay IA, Romano JP, Shaikh AM. Randomization tests under an approximate symmetry assumption. Econometrica. 2017;85(3):1013-30.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j862290346-1f3950c5-9824-45c4-8f6a-5c0dea8825c0">
					  <unstructured_citation>Mantovani RG, Rossi AL, Alcobaça E, Gertrudes JC, Junior SB, de Carvalho AC. Rethinking default values: A low cost and efficient strategy to define hyperparameters. arXiv:2008.00025 [cs.LG]; 2020.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j862290346-3d31f10a-f902-4836-be16-591a29d13cd2">
					  <unstructured_citation>Polkowski LT. Epistemic, default and dynamic logics. In: Logics for computer and data sciences, and artificial intelligence. Cham: Springer; 2022. p. 105-40.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j862290346-1ff2f547-fe04-4269-9947-ed6ffc6bd749">
					  <unstructured_citation>Phan HC, Dhar AS. Predicting pipeline burst pressures with machine learning models. Int J Press Vessel Pip. 2021;191:104384.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j862290346-db5fb073-0b83-4358-8cc1-8ec60ff5d960">
					  <unstructured_citation>Battaglia PW, Hamrick JB, Bapst V, Sanchez-Gonzalez A, Zambaldi V, Malinowski M, et al. Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261; 2018.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j862290346-f2e60997-ab9a-41df-a94c-d7ded9410dac">
					  <unstructured_citation>Masters P, Vered M. What’s the context? Implicit and explicit assumptions in model-based goal recognition. In: Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI). Montreal, Canada: AAAI Press; 2021. p. 4516-23.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j862290346-e16a6319-7599-4232-ae49-844915ae3e36">
					  <unstructured_citation>Van Zwet E. A default prior for regression coefficients. Stat Methods Med Res. 2019;28(12):3799-807.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j862290346-67d49b91-4483-4727-8df8-43c4abd47f10">
					  <unstructured_citation>Chollet F, Kalinowski T, Allaire JJ. Deep learning with R. New York: Simon and Schuster; 2022.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j862290346-d940993f-fa6c-4cb1-99a3-3870ac858a50">
					  <unstructured_citation>Feinman R, Lake BM. Learning inductive biases with simple neural networks. arXiv preprint arXiv:1802.02745; 2018.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j862290346-583f8dc7-84ff-44e6-a19e-d733121e0e3b">
					  <unstructured_citation>Okereke M, Keates S. Boundary conditions. In: Finite element applications: A practical guide to the FEM process. Cham: Springer International Publishing; 2018. p. 243-97.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j862290346-abf4864d-c76f-40b5-87c7-d90594509a2c">
					  <unstructured_citation>Atanasiu R. Hidden assumptions. In: Critical thinking for managers: Structured decision-making and persuasion in business. Cham: Springer; 2021. p. 13-26.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j862290346-61bd2873-d416-4128-8f6a-e32eb95a0b08">
					  <unstructured_citation>Min H, Tarmoun S, Vidal R, Mallada E. On the explicit role of initialization on the convergence and implicit bias of overparametrized linear networks. In: Proceedings of the 38th International Conference on Machine Learning (ICML). PMLR; 2021. p. 7760-8.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j862290346-9c553ede-51b6-4c0e-b531-ce989a496248">
					  <unstructured_citation>Boon M. How scientists are brought back into science-The error of empiricism. In: A critical reflection on automated science: Will science remain human? Cham: Springer; 2020. p. 43-65.</unstructured_citation>
											</citation>
          				</citation_list>
			</journal_article>
		</journal>
	</body>
</doi_batch>
