<?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-5gSf-1791483544-e964163524</doi_batch_id>
		<timestamp>1791483544</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>2024</year>
				</publication_date>
				<journal_volume>
					<volume>3</volume>
				</journal_volume>
				<issue>1</issue>
			</journal_issue>
			<journal_article publication_type="full_text">
				<titles>
					<title>Data Lineage and Scientific Traceability in Computational Materials Pipelines</title>
				</titles>
								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>Lucas</given_name>
            <surname>Andrade</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Mariana</given_name>
            <surname>Lopes</surname>
					</person_name>
          				</contributors>
								<publication_date>
					<year>2024</year>
				</publication_date>
				<doi_data>
					<doi>10.68159/e964163524</doi>
					<resource>https://iamrp.net/pub/journal/2/article/e964163524</resource>
				</doi_data>
				<citation_list>
          					<citation key="rk-10.68159/e964163524-6851ecd1-a36d-4b5c-b7e6-4a388737d466">
					  <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(1):54.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e964163524-118bf9e3-c4ac-48cf-bee0-6e2abd377c02">
					  <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/e964163524-83722671-2531-4c61-b31c-ba5d065a09cf">
					  <unstructured_citation>Zhong X, Gallagher B, Liu S, Kailkhura B, Hiszpanski A, Han TYJ. Explainable machine learning in materials science. npj Comput Mater. 2022;8(1):204.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e964163524-94f4bda5-e374-4bdc-879d-779a0448ee42">
					  <unstructured_citation>Xu P, Ji X, Li S, Lu W. Small data machine learning in materials science. npj Comput Mater. 2023;9(1):42.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e964163524-3617720d-3a15-4ee6-a264-68fb53f965a0">
					  <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>
											</citation>
          					<citation key="rk-10.68159/e964163524-208fb5a0-11c3-4b45-8a3a-f52b2ccede32">
					  <unstructured_citation>Luo S, Li T, Wang X, Faizan M, Zhang L. High‐throughput computational materials screening and discovery of optoelectronic semiconductors. Wiley Interdiscip Rev Comput Mol Sci. 2021;11(1):e1489.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e964163524-e1b93ea8-73f3-4e1f-96e4-97ecf456e5aa">
					  <unstructured_citation>Soedarmadji E, Stein HS, Suram SK, Guevarra D, Zhou J, Shinde A, et al. Tracking materials science data lineage to manage millions of materials experiments and analyses. npj Comput Mater. 2019;5(1):79.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e964163524-42a0091f-95ac-4727-b1b4-586b4f60a292">
					  <unstructured_citation>Bousquet RR, Oses C, Stein HS, Curtarolo S, Gregoire JM. ESAMP: Event-sourced architecture for materials provenance management and application to accelerated materials discovery. Digit Discov. 2023;2(5):1238-52.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e964163524-7503e15e-233b-4891-ac09-17588a28d20e">
					  <unstructured_citation>Wang X, Gong G, Li N. Artificial-intelligence-led revolution of construction materials: From molecules to Industry 4.0. Matter. 2023;6(6):1832-59.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e964163524-87808d04-d0fb-48fb-b13b-ee83bcfbe0b2">
					  <unstructured_citation>Xu P, Chen J, Li S, Lu W. Uncertainty and anharmonicity in thermally activated dynamics. Comput Mater Sci. 2021;193:110390.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e964163524-2af6adcd-46de-4903-8d9c-3f9c830fd9a8">
					  <unstructured_citation>Roy A, Balasubramanian G. Predictive descriptors in machine learning and data-enabled explorations of high-entropy alloys. Comput Mater Sci. 2021;190:110381.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e964163524-5524f7b3-ba8c-4da1-a449-49030240d599">
					  <unstructured_citation>Yan Y, Zhang L, Li S, Liang H, Qiao Z. Adsorption behavior of metal-organic frameworks: From single simulation, high-throughput computational screening to machine learning. Comput Mater Sci. 2021;190:110383</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e964163524-6a05389b-159f-4460-ab2c-065beb1dbd67">
					  <unstructured_citation>Hiraide K, Hirayama K, Endo K, Muramatsu M. Application of deep learning to inverse design of phase separation structure in polymer alloy. Comput Mater Sci. 2021;190:110278.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e964163524-4733c9a0-b667-461f-bc28-a86c2a9043d2">
					  <unstructured_citation>Szymanski NJ, Bartel CJ, Zeng Y, Tu Q, Ceder G. Autonomous experimentation systems for materials development: A community perspective. Matter. 2021;4(9):2702-26.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e964163524-9deed802-7fa0-4191-afcc-c050cf33217b">
					  <unstructured_citation>MacLeod BP, Parlane FGL, Brown AK, Hein JE, Berlinguette CP. What is a minimal working example for a self-driving laboratory? Matter. 2022;5(4):1141-56.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e964163524-10d4c762-c4cb-41d0-bc7c-17bda98c3cdf">
					  <unstructured_citation>Roch LM, Häse F, Kreisbeck C, Tamayo-Mendoza T, Yunker LPE, Hein JE, et al. ChemOS 2.0: An orchestration architecture for chemical self-driving laboratories. Matter. 2024;7(5):1883-97.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e964163524-e236f8cb-8ee4-45d8-ae9a-0c899a4b462e">
					  <unstructured_citation>Szymanski NJ, Rendy B, Fei Y, Kumar RE, He T, Milikisiyants D, et al. An autonomous laboratory for the accelerated synthesis of novel materials. Nat Commun. 2023;624(1):86-91.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e964163524-701cb042-929f-4944-8f8e-d39a961af53c">
					  <unstructured_citation>Tagade PM, Adiga SP, Pandian S, Kolake SM, Song S, Joshi S. Attribute driven inverse materials design using deep learning Bayesian framework. npj Comput Mater. 2019;5(1):103.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e964163524-da1200e3-a2c3-4844-b692-ec09dae22a9b">
					  <unstructured_citation>Cui Z, Gao T, Talamadupula K, Ji Q. Knowledge-augmented deep learning and its applications: A survey. IEEE Trans Neural Netw Learn Syst. 2023;36(2):2133-53.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e964163524-0e094e5e-e0dd-479d-b8bb-1c6c2536b91d">
					  <unstructured_citation>Mussmann S, Liang P. On the relationship between data efficiency and error for uncertainty sampling. In International Conference on Machine Learning. PMLR; 2018. p. 3674-82.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e964163524-fcb5dd6e-dee8-4fac-967c-84973c5e5613">
					  <unstructured_citation>Chen C, Zuo Y, Ye W, Li J, Deng Z, Ong SP. AI applications through the whole life cycle of material discovery. Matter. 2020;3(4):991-1014.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e964163524-24d02f92-e241-4b9f-aa1f-15277baeaa7c">
					  <unstructured_citation>Court CJ, Cole JM. Propnet: A knowledge graph for materials science. Matter. 2020;2(3):551-61.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e964163524-9f58f98f-cbbf-44ec-960b-e42c50cb3304">
					  <unstructured_citation>Oliveira L, Zaera-Polo J, Calatayud M, Kisielowski C, Specht P, Algarra AG, et al. Accelerating the adoption of research data management strategies. Matter. 2022;6(3):696-709.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e964163524-e4f79e34-8a17-4643-9261-306c600ea868">
					  <unstructured_citation>Muroga A. Recommender system for discovery of inorganic compounds. npj Comput Mater. 2022;8(1):249.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e964163524-53236cc5-7d0a-4034-b738-6815e85a78c5">
					  <unstructured_citation>Zhang S, He J, Zhao W, Liu P. Predicting lattice thermal conductivity via machine learning: A perspective. npj Comput Mater. 2023;9(1):19.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e964163524-99a63832-696c-4731-94b5-9ea51c0f23f7">
					  <unstructured_citation>Zhou X, Li L, Zhang Z, Li W, Chen Y, Zhao M, et al. Nobility vs. mobility: Insights into molten salt corrosion mechanisms of high-entropy alloys via high-throughput experiments and machine learning. Matter. 2024;7(5):1898-918.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e964163524-600c27b4-d57a-4c45-8b5b-543baec8d72d">
					  <unstructured_citation>Statt A, Kovalenko A, Tron A, Curtarolo S, Amsler M, Goedecker S, et al. Physical computing for materials acceleration platforms. Matter. 2023;6(3):710-33.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e964163524-58cf25f1-efac-4488-bc5b-c8f5214fdf43">
					  <unstructured_citation>Ghiringhelli LM, Levchenko SV, Heinen J, Impagnatiello A, Nardi F, Vitale V, et al. Brokering between tenants for an international materials acceleration platform. Matter. 2023;6(10):3345-67.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e964163524-d6a1c4e0-0160-493f-9c13-7d7d3fe244d0">
					  <unstructured_citation>Aykol M, Montoya JH, Hummelshøj J. The materials research platform: Defining the requirements from user stories. Matter. 2019;1(6):1433-8.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e964163524-6e2877c4-8a61-4532-abe2-a72fe5ca4aec">
					  <unstructured_citation>Frey CE, Muñoz JA, Amsler M. Mkite: A distributed computing platform for high-throughput materials simulations. Comput Mater Sci. 2023;230:112480.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e964163524-7a3da1a2-ea47-4c58-940e-ba374ce01562">
					  <unstructured_citation>Rosen AS, Iyer SM, Ray D, Yao Z, Aspuru-Guzik A, Gagliardi L, et al. Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery. Matter. 2021;4(5):1578-97.</unstructured_citation>
											</citation>
          				</citation_list>
			</journal_article>
		</journal>
	</body>
</doi_batch>
