<?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-VaS7-1791515266-j756092814</doi_batch_id>
		<timestamp>1791515266</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>Algorithmic Novelty vs Chemical Novelty: Rethinking Innovation Metrics</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>2023</year>
				</publication_date>
				<doi_data>
					<doi>10.68159/j756092814</doi>
					<resource>https://iamrp.net/pub/journal/2/article/j756092814</resource>
				</doi_data>
				<citation_list>
          					<citation key="rk-10.68159/j756092814-ba5feaaa-0f61-46ac-ae48-472c59644ba5">
					  <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(1):83.</unstructured_citation>
						 <doi>10.1038/s41524-019-0221-0</doi> 					</citation>
          					<citation key="rk-10.68159/j756092814-b59de1d8-b05b-4b3b-89b7-c3be106c0333">
					  <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>
						 <doi>10.1038/s41524-017-0056-5</doi> 					</citation>
          					<citation key="rk-10.68159/j756092814-35af3cbe-054b-469c-a7c6-001ef08d9a1e">
					  <unstructured_citation>Xu P, Ji X, Li M, Lu W. Small data machine learning in materials science. npj Comput Mater. 2023;9(1):42.</unstructured_citation>
						 <doi>10.1038/s41524-023-01000-z</doi> 					</citation>
          					<citation key="rk-10.68159/j756092814-8cd44c4d-6fd6-4daa-9770-2ea89a47864c">
					  <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/j756092814-6274e63e-e657-40ca-9ed0-322cfac30193">
					  <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(1):59.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j756092814-4c5d6bd0-fec8-474b-89c5-4646e8de17ac">
					  <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/j756092814-a8cd62cd-7d2c-44ca-947c-8a03c884976d">
					  <unstructured_citation>Merchant A, Batzner S, Schoenholz SS, Aykol M, Cheon G, Cubuk ED. Scaling deep learning for materials discovery. Nature. 2023;624(7991):80-5.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j756092814-8d8d8a11-c332-484a-aeb3-5b884e523168">
					  <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/j756092814-56cad14b-dc2a-4b91-877c-86a53ab975f8">
					  <unstructured_citation>Griesemer SD, Xia Y, Wolverton C. Accelerating the prediction of stable materials with machine learning. Nat Comput Sci. 2023;3(11):934-45.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j756092814-d473f0de-2f1f-4aaf-9e4c-a911c0d34dc2">
					  <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/j756092814-b290fbc4-2c6a-496e-b1e6-9742450c6e4a">
					  <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(1):84.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j756092814-d579ef5a-e5c9-49b8-8d26-bbcd4083f7f8">
					  <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/j756092814-cb14961a-b340-4b12-90a9-c483e859ba41">
					  <unstructured_citation>Reiser P, Neubert M, Eberhard A, Torresi L, Zhou C, Shao C, et al. Graph neural networks for materials science and chemistry. Commun Mater. 2022;3(1):93.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j756092814-95a7cd28-ccd5-4b82-8715-5409212656ca">
					  <unstructured_citation>Umehara M, Stein HS, Guevarra D, Newhouse PF, Boyd DA, Gregoire JM. Analyzing machine learning models to accelerate generation of fundamental materials insights. npj Comput Mater. 2019;5(1):34.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j756092814-e05e7051-99a0-45b6-aa85-61da042b7f81">
					  <unstructured_citation>Chang R, Wang YX, Ertekin E. Towards overcoming data scarcity in materials science: unifying models and datasets with a mixture of experts framework. npj Comput Mater. 2022;8(1):242.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j756092814-1720fb55-8d91-4985-8eae-522666774784">
					  <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/j756092814-b9839232-28c6-4984-a7cc-0d14b2e62ee9">
					  <unstructured_citation>Batra R, Song L, Ramprasad R. Emerging materials intelligence ecosystems propelled by machine learning. Nat Rev Mater. 2021;6(8):655-78.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j756092814-64867c4f-ef8e-4511-885d-b0b122331751">
					  <unstructured_citation>Nyshadham C, Rupp M, Bekker B, Shapeev AV, Mueller T, Rosenbrock CW, et al. Machine-learned multi-system surrogate models for materials prediction. npj Comput Mater. 2019;5(1):51.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j756092814-ca752aa4-64d8-42d6-9078-ab8a18daa82b">
					  <unstructured_citation>Kailkhura B, Gallagher B, Kim S, Hiszpanski A, Han TY. 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/j756092814-33a675bd-dee7-4e54-aa46-f1d34ba4b915">
					  <unstructured_citation>Li Z, Yoon J, Zhang R, Rajabipour F, Srubar WV III, Dabo I, et al. Machine learning in concrete science: applications, challenges, and best practices. npj Comput Mater. 2022;8(1):127.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j756092814-2917d74f-c45a-4f32-8a67-24d7937715ce">
					  <unstructured_citation>Hatakeyama-Sato K, Oyaizu K. Integrating multiple materials science projects in a single neural network. Commun Mater. 2020;1(1):49.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j756092814-b9804fbd-f09e-407c-86d4-7a22cb2a6d2b">
					  <unstructured_citation>Fujinuma N, DeCost B, Hattrick-Simpers J, Lofland SE. Why big data and compute are not necessarily the path to big materials science. Commun Mater. 2022;3(1):59.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j756092814-83052859-12cf-4e8f-9ae0-53683a23bbd7">
					  <unstructured_citation>Feng S, Fu H, Zhou H, Wu Y, Lu Z, Dong H. A general and transferable deep learning framework for predicting phase formation in materials. npj Comput Mater. 2021;7(1):10.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j756092814-331d7de1-8721-491a-8611-13cdad54076c">
					  <unstructured_citation>Li K, DeCost B, Choudhary K, Greenwood M, Hattrick-Simpers J. A critical examination of robustness and generalizability of machine learning prediction of materials properties. npj Comput Mater. 2023;9(1):55.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j756092814-c9c82441-1c99-4dba-9371-89952e89702b">
					  <unstructured_citation>Huo H, Rong Z, Kononova O, Sun W, Botari T, He T, et al. Semi-supervised machine-learning classification of materials synthesis procedures. npj Comput Mater. 2019;5(1):62.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j756092814-ef44f100-80f2-4348-9572-00a15cee3686">
					  <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/j756092814-10acdb35-6512-4a7c-8391-a592b51d7cd6">
					  <unstructured_citation>Unkhovskyi AG, Draxl C, Schleder GR, Botti S, Marques MAL. Machine-learning interatomic potentials for materials science. Acta Mater. 2021;214:116980.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j756092814-bc0bf6ea-a17b-4ec5-a22b-5c7dc88655b4">
					  <unstructured_citation>Rupp M, Tkatchenko A, Müller K-R, von Lilienfeld OA. MatCALO: Knowledge-enabled machine learning in materials science. Comput Mater Sci. 2019;164:82-90.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j756092814-95dcbab4-b470-4e11-b0fa-b3a0e8cc0b18">
					  <unstructured_citation>Li J, Liu Y, Guan X, Head-Gordon T. Leveraging machine learning in the innovation of functional materials. Matter. 2023;6(8):2553-5.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j756092814-a0c87945-8ae0-4b61-a451-f76e906cc99e">
					  <unstructured_citation>Butler KT, Davies DW, Ganose A, Gaultois MW, Gopalakrishnan G, Scanlon DO, et al. Teaching machine learning to materials scientists: Lessons from hosting tutorials and competitions. Matter. 2022;5(6):1620-2.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j756092814-91afc601-ee10-4f4b-874e-a619201ed9de">
					  <unstructured_citation>Liu B, Xu Z, Chen C, Pang K, Wang Y, Ruan Q. Effect of tool edge radius on material removal mechanism of single-crystal silicon: Numerical and experimental study. Comput Mater Sci. 2019;163:127-33.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j756092814-8303578f-f10c-47e6-b8ff-924a9bda447b">
					  <unstructured_citation>Hassanpour A, Clement C, Ju S, Xu S, Lutfiyya S, Liu Y, et al. Intelligent microfluidics: The convergence of machine learning and microfluidics in materials science and biomedicine. Matter. 2020;3(5):1394-431.</unstructured_citation>
											</citation>
          				</citation_list>
			</journal_article>
		</journal>
	</body>
</doi_batch>
