<?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-YGE7-1791515267-w895377926</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>2024</year>
				</publication_date>
				<journal_volume>
					<volume>3</volume>
				</journal_volume>
				<issue>2</issue>
			</journal_issue>
			<journal_article publication_type="full_text">
				<titles>
					<title>Knowledge Graphs for Materials Discovery: Data Structuring, Reasoning, and Applications</title>
				</titles>
								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>Maria</given_name>
            <surname>Hernandez</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Carlos</given_name>
            <surname>Vega</surname>
					</person_name>
          				</contributors>
								<publication_date>
					<year>2024</year>
				</publication_date>
				<doi_data>
					<doi>10.68159/w895377926</doi>
					<resource>https://iamrp.net/pub/journal/2/article/w895377926</resource>
				</doi_data>
				<citation_list>
          					<citation key="rk-10.68159/w895377926-163a9b27-fed6-44e1-8764-e7a503113f73">
					  <unstructured_citation>Venugopal V, Olivetti E. MatKG: An autonomously generated knowledge graph in Material Science. Sci Data. 2024;11.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w895377926-61363306-d4cc-4ec5-9f0a-16dbd091f866">
					  <unstructured_citation>Bai J, Mosbach S, Taylor CJ, Karan D, Lee KF, Rihm SD, et al. A dynamic knowledge graph approach to distributed self-driving laboratories. Nat Commun. 2024;15(642).</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w895377926-9c58930c-7c78-4cea-9f13-9a39b2570387">
					  <unstructured_citation>Bayerlein B, Schilling M, von Hartrott P, Waitelonis J. Semantic integration of diverse data in materials science: Assessing Orowan strengthening. Sci Data. 2024;11(434).</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w895377926-2b0fcc10-7fbb-45c0-a816-b5f2f06d21c5">
					  <unstructured_citation>Statt MJ, Rohr BA, Guevarra D, Breeden J, Suram SK, Gregoire JM. The materials experiment knowledge graph. Digit Discov. 2023;2(4):909-14.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w895377926-3ec58a4e-4aba-4c80-b64a-fa767d5bcc8f">
					  <unstructured_citation>Zhang Y, Chen F, Liu Z, Ju Y, Cui D, Zhu J,et al. A materials terminology knowledge graph automatically constructed from text corpus. Sci Data. 2024;11(600).</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w895377926-53014979-3f5e-4c85-9170-262eccb5dfd4">
					  <unstructured_citation>Stear BJ, Mohseni Ahooyi T, Simmons JA, Kollar C, Hartman L, Beigel K, et al. Petagraph: A large-scale unifying knowledge graph framework for integrating biomolecular and biomedical data. Sci Data. 2024;11(1338).</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w895377926-c3c00896-353c-44b5-b8ec-d0effd9ee83d">
					  <unstructured_citation>Gupta T, Zaki M, Krishnan NMA, Mausam. MatSciBERT: A materials domain language model for text mining and information extraction. npj Comput Mater. 2022;8(102).</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w895377926-48c1c7e6-805b-4faa-ab09-82fd8d4477fe">
					  <unstructured_citation>Mrdjenovich D, Horton MK, Montoya JH, Legaspi CM, Dwaraknath S, Tshitoyan V, et al. Propnet: A knowledge graph for materials science. Matter. 2020;2(2).</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w895377926-d1938bea-3457-4037-9d6c-f9471496d317">
					  <unstructured_citation>Chandak P, Huang K, Zitnik M. Building a knowledge graph to enable precision medicine. Sci Data. 2023;10(67).</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w895377926-54b88bdb-78dd-4d6d-8bd3-46b48d7cdc10">
					  <unstructured_citation>Fang Y, Zhang Q, Zhang N, Chen Z, Zhuang X, Shao X, et al. Knowledge graph-enhanced molecular contrastive learning with functional prompt. Nat Mach Intell. 2023;5:542-53.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w895377926-8869df26-e042-4066-b482-a669690643e0">
					  <unstructured_citation>Li H, Zhang R, Min Y, Ma D, Zhao D, Zeng J. A knowledge-guided pre-training framework for improving molecular representation learning. Nat Commun. 2023;14(7568).</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w895377926-a02b96d1-db97-4ad9-b392-47b1eccf3089">
					  <unstructured_citation>Pyzer-Knapp EO, Pitera JW, Staar PWJ, Takeda S, Laino T, Sanders DP, et al. Accelerating materials discovery using artificial intelligence, high performance computing and robotics. npj Comput Mater. 2022;8(84).</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w895377926-c906edef-7f22-4c3e-8ebe-42d007e9e79c">
					  <unstructured_citation>Lemm D, von Rudorff GF, von Lilienfeld OA. Impact of noise on inverse design: the case of NMR spectra matching. Digit Discov. 2024;3.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w895377926-d5bd66c4-f95c-4b7a-89cf-f3907fb87f40">
					  <unstructured_citation>Liu S, Su Y, Yin H, Zhang D, He J, Huang H, et al. An infrastructure with user-centered presentation data model for integrated management of materials data and services. npj Comput Mater. 2021;7(88).</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w895377926-dba6790f-c2a6-43d4-b09f-9d7cc3d17468">
					  <unstructured_citation>Hatakeyama-Sato K, Oyaizu K. Integrating multiple materials science projects in a single neural network. Commun Mater. 2020;1(49).</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w895377926-3b7bc0f2-d3a4-4064-b76a-a07720f01520">
					  <unstructured_citation>Jablonka KM, Ai Q, Al-Feghali A, Badhwar S, Bocarsly JD, Bran AM, et al. 14 examples of how LLMs can transform materials science and chemistry: A reflection on a large language model hackathon. Digit Discov. 2023;2.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w895377926-399fad63-1967-45f2-8709-ba6982c3a11f">
					  <unstructured_citation>Cavalleri E, Cabri A, Soto-Gomez M, Bonfitto S, Perlasca P, Gliozzo J, et al. An ontology-based knowledge graph for representing interactions involving RNA molecules. Sci Data. 2024;11(906).</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w895377926-d3fb5aff-48f5-4d90-8980-288fe7ae1dce">
					  <unstructured_citation>Statt MJ, Rohr BA, Brown K, Guevarra D, Hummelshøj J, Hung L, et al. ESAMP: Event-sourced architecture for materials provenance management and application to accelerated materials discovery. Digit Discov. 2023;2(4):1078-88.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w895377926-8ce99a0a-8407-4272-afc9-36dc0f72ab69">
					  <unstructured_citation>Canty RB, Koscher BA, McDonald MA, Jensen KF. Integrating autonomy into automated research platforms. Digit Discov. 2023;2.</unstructured_citation>
						 <doi>10.1039/D3DD00135K</doi> 					</citation>
          					<citation key="rk-10.68159/w895377926-35fdf326-d89b-4a29-b69e-cfa40c3d5f6d">
					  <unstructured_citation>Yan R, Jiang X, Wang W, Dang D, Su Y. Materials information extraction via automatically generated corpus. Sci Data. 2022;9(401).</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w895377926-55d0997a-0c12-4adc-a198-d0677d444e12">
					  <unstructured_citation>Hira K, Zaki M, Sheth D, Mausam, Krishnan NMA. Reconstructing the materials tetrahedron: Challenges in materials information extraction. Digit Discov. 2024;3(15):1021-37.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w895377926-db108f5c-77ca-4720-91e5-f5df11a3c2bb">
					  <unstructured_citation>Rydholm E, Bastys T, Svensson E, Kannas C, Engkvist O, Kogej T. Expanding the chemical space using a chemical reaction knowledge graph. Digit Discov. 2024;3.</unstructured_citation>
						 <doi>10.1039/D3DD00230F</doi> 					</citation>
          					<citation key="rk-10.68159/w895377926-62317842-76db-449a-8f32-9bb78ea46cb0">
					  <unstructured_citation>Kondinski A, Rutkevych P, Pascazio L, Tran DN, Farazi F, Ganguly S, et al. Knowledge graph representation of zeolitic crystalline materials. Digit Discov. 2024;3(10):2070-84.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w895377926-4250812d-d08e-4916-9050-2c77747cccad">
					  <unstructured_citation>Liu J, Qian Q. Reinforcement learning-based knowledge graph reasoning for aluminum alloy applications. Comput Mater Sci. 2023;221.</unstructured_citation>
						 <doi>10.1016/j.commatsci.2023.112075</doi> 					</citation>
          					<citation key="rk-10.68159/w895377926-31a69fac-14aa-4027-96ac-1cf7bcaed918">
					  <unstructured_citation>Shao X, Li C, Yang H, Lu X, Liao J, Qian J, et al. Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk. Nat Commun. 2022;13(4429).</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w895377926-0782139c-16c5-405c-a51a-4e60759e455f">
					  <unstructured_citation>Boudin M, Diallo G, Drancé M, Mougin F. The OREGANO knowledge graph for computational drug repurposing. Scientific Data. 2023;10(871).</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w895377926-038e8b5d-713a-47fd-aae3-f9844c9ecbb6">
					  <unstructured_citation>Gaudry A, Pagni M, Mehl F, Moretti S, Quiros-Guerrero LM, Cappelletti L, et al. A sample-centric and knowledge-driven computational framework for natural products drug discovery. ACS Cent Sci. 2024;10(3).</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w895377926-7486d5ad-c458-497f-907f-d68070d8b38e">
					  <unstructured_citation>Pelkie BG, Pozzo LD. The laboratory of Babel: Highlighting community needs for integrated materials data management. Digit Discovery. 2023;2. https://pubs.rsc.org/en/content/articlelanding/2023/dd/d3dd00022b#:~:text=Article%20information-,DOI,https%3A//,-Article%20type</unstructured_citation>
						 <doi>10.1039/D3DD00022B</doi> 					</citation>
          					<citation key="rk-10.68159/w895377926-69aba510-71cb-4cf3-82df-736a73e7e572">
					  <unstructured_citation>Cavalleri E, Cabri A, Soto-Gómez M, Bonfitto S, Perlasca P, Gliozzo J, et al. An open source knowledge graph ecosystem for the life sciences. Sci Data. 2024;11(363).</unstructured_citation>
											</citation>
          				</citation_list>
			</journal_article>
		</journal>
	</body>
</doi_batch>
