<?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-OrKf-1791515267-f747134218</doi_batch_id>
		<timestamp>1791515267</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>2</issue>
			</journal_issue>
			<journal_article publication_type="full_text">
				<titles>
					<title>Simulation Priors in Machine Learning Materials Models: Hidden Physics Assumptions</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>2023</year>
				</publication_date>
				<doi_data>
					<doi>10.68159/f747134218</doi>
					<resource>https://iamrp.net/pub/journal/2/article/f747134218</resource>
				</doi_data>
				<citation_list>
          					<citation key="rk-10.68159/f747134218-6982f162-e72e-42da-8f28-40f88affc6ae">
					  <unstructured_citation>Schmidt J, Marques MRG, Botti S, Marques MAL. Recent advances and applications of machine learning in solid-state materials science. npj Comput Mater. 2019;5:83.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/f747134218-0c676970-6208-4902-b85f-ff979e74d605">
					  <unstructured_citation>Ramprasad R, Batra R, Pilania G, Mannodi-Kanakkithodi A, Kim C. Machine learning in materials informatics: recent applications and prospects. npj Comput Mater. 2017;3:54.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/f747134218-ec3cdc21-23d2-4dff-93f3-7bb9119dc8fe">
					  <unstructured_citation>Fung V, Zhang J, Juarez E, Sumpter BG. Benchmarking graph neural networks for materials chemistry. npj Comput Mater. 2021;7(1):84.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/f747134218-b4968728-72ef-4b95-b871-94aec7ae50e7">
					  <unstructured_citation>Kojic M, Zecevic M, Cukovic M, Grujovic N, Reese SP, Knezevic M. Graph neural networks for efficient learning of mechanical properties of polycrystals. Comput Mater Sci. 2023;217:111894.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/f747134218-19febe07-e4ff-42b3-ac82-599975c82f86">
					  <unstructured_citation>Schmidt J, Shi H-L, Isnard O, Marques MAL, Botti S. Crystal graph attention networks for the prediction of stable materials. Sci Adv. 2021;7(49):eabi7948.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/f747134218-f1d246c2-45a3-4c04-bb17-bc2ae4c6dfec">
					  <unstructured_citation>Stach E, DeCost B, Kusne AG, Hattrick-Simpers J, Brown KA, Reyes KG, et al. Autonomous experimentation systems for materials development: A community perspective. Matter. 2021;4(9):2702-26.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/f747134218-585b28c9-f41f-4366-9955-6ab1ab0708f2">
					  <unstructured_citation>Szymanski NJ, Rendy B, Fei Y, Kumar RE, He T, Milsted D, McDermott MJ, et al. An autonomous laboratory for the accelerated synthesis of inorganic materials. Nature. 2023;624(7990):86-91.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/f747134218-005e538c-35b5-4106-8c01-db0a88e56f99">
					  <unstructured_citation>Kusne AG, Yu H, Wu C, Zhang H, Hattrick-Simpers J, DeCost B, et al. On-the-fly closed-loop materials discovery via Bayesian active learning. Nat Commun. 2020;11(1):5966</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/f747134218-93f45c47-26cc-4654-bfec-185c8102fd5c">
					  <unstructured_citation>Wolloch M, Losi G, Chehaimi O, Yalcin F, Ferrario M, Righi MC. High-throughput generation of potential energy surfaces for solid interfaces. Comput Mater Sci. 2022;207:111302.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/f747134218-55526f23-1be4-4568-b453-a720c1fd9b0a">
					  <unstructured_citation>Lookman T, Balachandran PV, Xue D, Yuan R. Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design. npj Comput Mater. 2019;5(1):21.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/f747134218-9ced676a-e107-4bd4-846e-ea8d8259585e">
					  <unstructured_citation>Kalantre S, Ray S, Mishra AA, Ndayishimiye A, Bud’ko SL, Canfield PC, et al. Closed-loop superconducting materials discovery. npj Comput Mater. 2023;9(1):156.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/f747134218-e37fe818-c319-45ee-9f8c-d0b08cd88b9d">
					  <unstructured_citation>Gupta V, Choudhary K, Tavazza F, Campbell C, Liao W-k, 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>
											</citation>
          					<citation key="rk-10.68159/f747134218-167a174e-54cb-497b-aa24-1cbc52c8a203">
					  <unstructured_citation>Pun GPP, Yamakov V, Mishin Y. Physically informed artificial neural networks for atomistic modeling of materials. Nat Commun. 2019;10(1):2339.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/f747134218-03e1e4b3-0cd6-4a92-a600-9698f96c330a">
					  <unstructured_citation>Yaghoobi M, Adnan A, Hartmaier A. Analyses of internal structures and defects in materials using physics-informed neural networks. Sci Adv. 2022;8(7):eabk0644.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/f747134218-ff9fb399-468f-450e-9274-6f4a9e38e050">
					  <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>
											</citation>
          					<citation key="rk-10.68159/f747134218-fa4c66ec-ba40-4bd7-b086-04e5d795b7d7">
					  <unstructured_citation>Jha D, Gupta V, Ward L, Yang Z, Wolverton C, Foster I, et al. Attribute driven inverse materials design using deep learning Bayesian framework. npj Comput Mater. 2019;5(1):127.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/f747134218-2421aede-fdf8-4712-9480-c000f4ae8497">
					  <unstructured_citation>Attari V, Khatamsaz D, Allaire D, Arroyave R. Towards inverse microstructure-centered materials design using generative phase-field modeling and deep variational autoencoders. Acta Mater. 2023;259:119204.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/f747134218-20765819-ec35-425d-a9e2-dba9bb817d37">
					  <unstructured_citation>Deng C, Liu C, Hu J, Huang K, Zhao Y, Ye W, et al. Rapid inverse design of metamaterials based on prescribed mechanical behavior through machine learning. Nat Commun. 2023;14(1):5565.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/f747134218-5f45f41f-bc50-4df4-814d-8e1e95eb1b33">
					  <unstructured_citation>Kavalsky L, Hegde VI, Hummelshøj JS, Johnson MS, Meredig B, Viswanathan V. By how much can closed-loop frameworks accelerate computational materials discovery?. Digit Discov. 2023;2(4):1168-77.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/f747134218-90c06da3-a081-438f-a79a-3a4421115513">
					  <unstructured_citation>Choubisa H, Berman A, Kenis PJA, Ertekin E. Closed-Loop error-correction learning accelerates experimental discovery of thermoelectric materials. Adv Mater. 2023;35(40):2302575.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/f747134218-bd087ead-ad96-492a-9cb3-9867b296904e">
					  <unstructured_citation>Ament S, Amsler M, Sutherland DR, Chang M-C, Guevarra D, Connolly AB, et al. Autonomous materials synthesis via hierarchical active learning of nonequilibrium phase diagrams. Sci Adv. 2021;7(51):eabg4930.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/f747134218-488dff2f-4ebb-4ef2-8275-832a31ed4177">
					  <unstructured_citation>Zhao Y, Krishnan NMA, Bauchy M, Lu W. Small data machine learning in materials science. npj Comput Mater. 2023;9(1):42.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/f747134218-8405acb8-e8a3-4324-b076-487c353ca5e1">
					  <unstructured_citation>Choudhary K, DeCost B, Chen C, Jain A, Tavazza F, Cohn R, et al. Recent advances and applications of deep learning methods in materials science. npj Comput Mater. 2022;8(59).</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/f747134218-a1bdc482-3ba7-4f26-a694-899d790be69b">
					  <unstructured_citation>Kailkhura B, Gallagher B, Kim S, Hiszpanski A, Han TY-J. Reliable and explainable machine-learning methods for accelerated material discovery. npj Comput Mater. 2019;5(1):108.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/f747134218-a5d72a3c-4d88-4e9c-afae-9834c5c9393d">
					  <unstructured_citation>Zhong X, Gallagher B, Liu S, Kailkhura B, Hiszpanski A, Han TY-J. Explainable machine learning in materials science. npj Comput Mater. 2022;8(1):204.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/f747134218-7df2e7df-906d-492f-9609-f14c62c71398">
					  <unstructured_citation>Pilania G. Machine learning in materials science: From explainable predictions to autonomous design. Comput Mater Sci. 2021;193:110360.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/f747134218-bbe9e0dd-8ca9-4380-a845-867815a52c46">
					  <unstructured_citation>Boyce B, Dingreville R, Desai S, Walker E, Shilt T, Bassett KL, et al. Machine learning for materials science: Barriers to broader adoption. Matter. 2023;6(5):1320-3.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/f747134218-304effb1-f734-42ec-b9fc-e6eb1577bec0">
					  <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>
											</citation>
          					<citation key="rk-10.68159/f747134218-82f1b8e8-4cd5-4850-a663-570fd6c4056e">
					  <unstructured_citation>Goodall REA, Lee AA. Predicting materials properties without crystal structure: deep representation learning from stoichiometry. Nat Commun. 2020;11(1):6280.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/f747134218-8b60308b-c703-43a6-a1f9-ef355039e706">
					  <unstructured_citation>Fung V, Hu G, Ganesh P, Sumpter BG. Distributed representations of atoms and materials for machine learning. npj Comput Mater. 2022;8(1):40.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/f747134218-3db1c2c7-905f-4829-8992-e749ec8cba26">
					  <unstructured_citation>Cubuk ED, Schoenholz SS, Rieser JM, Malone B, Rottler J, Durian DJ, et al. Inverse design of solid-state materials via a continuous representation. Matter. 2019;1(5):1370-84.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/f747134218-a1450886-4c58-455f-827f-622361aece0d">
					  <unstructured_citation>Coli GM, Baldi E, de Oliveira LFCF, Dijkstra M. Inverse design of soft materials via a deep learning–based evolutionary strategy. Sci Adv. 2022;8(3):eabj6731.</unstructured_citation>
											</citation>
          				</citation_list>
			</journal_article>
		</journal>
	</body>
</doi_batch>
