<?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-uglO-1791515265-a122684570</doi_batch_id>
		<timestamp>1791515265</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>2025</year>
				</publication_date>
				<journal_volume>
					<volume>4</volume>
				</journal_volume>
				<issue>2</issue>
			</journal_issue>
			<journal_article publication_type="full_text">
				<titles>
					<title>Runaway Discovery Trajectories: Intervention Thresholds in Self-Reinforcing Materials AI Systems</title>
				</titles>
								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>Claire</given_name>
            <surname>Martin</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Julien</given_name>
            <surname>Robert</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Sophie</given_name>
            <surname>Bernard</surname>
					</person_name>
          				</contributors>
								<publication_date>
					<year>2025</year>
				</publication_date>
				<doi_data>
					<doi>10.68159/a122684570</doi>
					<resource>https://iamrp.net/pub/journal/2/article/a122684570</resource>
				</doi_data>
				<citation_list>
          					<citation key="rk-10.68159/a122684570-cfad5551-f720-4ab6-bc35-3de362b877bd">
					  <unstructured_citation>Chen C, Ye W, Zuo Y, Zheng C, Qian Z, Stoudenmire EM, et al. Machine learning unifies the modeling of materials and molecules. Sci Adv. 2017;3(12):e1701816.</unstructured_citation>
						 <doi>10.1126/sciadv.1701816</doi> 					</citation>
          					<citation key="rk-10.68159/a122684570-836e6274-b363-4775-8ade-151c993e92c9">
					  <unstructured_citation>Kumar A, Ricci F, Chen Y, Shen S, Kumar A, Kumar A, et al. Self-driving laboratory for accelerated discovery of thin-film materials. Sci Adv. 2020;6(25):eaaz8867.</unstructured_citation>
						 <doi>10.1126/sciadv.aaz8867</doi> 					</citation>
          					<citation key="rk-10.68159/a122684570-d8c10e6d-7723-4e99-a880-037ad58c05fc">
					  <unstructured_citation>Ma KY, Huo H, Golmira A, Hu J, Hu Z, Krishnan S, et al. Leveraging data mining, active learning, and domain adaptation for materials discovery. Sci Adv. 2025;11(14):eadr9038.</unstructured_citation>
						 <doi>10.1126/sciadv.adr9038</doi> 					</citation>
          					<citation key="rk-10.68159/a122684570-ac499cd2-e533-4fac-9c7f-618618e75162">
					  <unstructured_citation>He J, Tao L, Murdock JR, Li Y. Machine learning enables interpretable discovery of innovative polymers for gas separation membranes. Sci Adv. 2022;8(29):eabn9545.</unstructured_citation>
						 <doi>10.1126/sciadv.abn9545</doi> 					</citation>
          					<citation key="rk-10.68159/a122684570-50ff3d27-e8dc-4687-84b5-9dd5b8781fa3">
					  <unstructured_citation>Brown KA, Yang Y, Ramachandran A, Chatterjee A, Chen C, Hu Y, et al. AI applications through the whole life cycle of material discovery. Matter. 2020;3(3):564-92.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/a122684570-eda4da8e-704e-4c2b-b086-3be8810492e5">
					  <unstructured_citation>Ozin GA, Siler T, Qian C, Zhou W. The curiosity-creativity element in HI-AI materials discovery. Matter. 2024;7(3):715-7.</unstructured_citation>
						 <doi>10.1016/j.matt.2024.01.001</doi> 					</citation>
          					<citation key="rk-10.68159/a122684570-ed24294d-6641-406f-a75f-8f2b90a117b9">
					  <unstructured_citation>Horton MK, Häse F, Aldeghi M, Musil F, Booth DW, Clarysse B, et al. Has generative artificial intelligence solved inverse materials design? Matter. 2024;7(8):2470-2.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/a122684570-0d394176-0a8f-46e7-a217-8fdf400a1bb0">
					  <unstructured_citation>Brown KA, Ramachandran A, Chatterjee A, Chen C, Hu Y, Li J, et al. Can AI be an inventor in materials discovery? Matter. 2023;6(10):3183-5.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/a122684570-c39bd245-9883-4cfd-9574-678b7ea3f11f">
					  <unstructured_citation>Boyce BL, 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/a122684570-092ef971-d1aa-4bff-a96e-54ba04b8e4d5">
					  <unstructured_citation>Sivaraman G, Akimov AV, Neaton JB, Chan MKY. Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery. Matter. 2021;4(5):1578-97.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/a122684570-7043db41-f9a8-481a-b7c6-a19953866aab">
					  <unstructured_citation>Chen Y, Zhang J, Li H, Wang K, Liu M, Zhao S. Autonomous closed-loop exploration of composition-spread films for the anomalous Hall effect. npj Comput Mater. 2025;11(156).</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/a122684570-2bd4f3fd-0135-4b8c-a93b-77354ac21a07">
					  <unstructured_citation>Felis N, Dononelli W. FALCON: Fast active learning for machine learning potentials in atomistic and ab initio molecular dynamics simulations. npj Comput Mater. 2025.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/a122684570-f9f6860f-ca5f-4377-97aa-00063bc653a1">
					  <unstructured_citation>Mashhadimoslem H, Karimi P, Elkamel A, Yu A. Toward high entropy material discovery for energy applications using computational and machine learning methods. npj Comput Mater. 2025.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/a122684570-919c1318-b8d8-4ce6-a654-efff1433c6bb">
					  <unstructured_citation>Ren F, Ward L, Williams T, Baldo PM, Hattrick-Simpers J. A self-driving physical vapor deposition system making sample-specific decisions on the fly. npj Comput Mater. 2025;11(121).</unstructured_citation>
						 <doi>10.1038/s41524-025-01805-0</doi> 					</citation>
          					<citation key="rk-10.68159/a122684570-38cb5ea9-504c-47fd-88c0-0423789b91f3">
					  <unstructured_citation>Yao J, Wang Z, Wang J, Yu W, Chen Y, Li W, et al. Alloy design integrating natural language processing and machine learning: breakthrough development of low-cost, high-performance Ni-based single-crystal superalloys. npj Comput Mater. 2025.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/a122684570-d55b9db2-6898-49ee-b433-192ccc315eab">
					  <unstructured_citation>Kendall D, MacLeod BP, Parlane CFG, McCulloch MD, Lugier R, Schrek B, et al. Active learning guides discovery of a champion four-metal perovskite for indoor photovoltaics. Nat Mater. 2024;23:74-83.</unstructured_citation>
						 <doi>10.1038/s41563-023-01707-w</doi> 					</citation>
          					<citation key="rk-10.68159/a122684570-ab52135b-f5c0-4f3e-8e18-2e9933b5a053">
					  <unstructured_citation>Rao Z, Lu P, McKone JR, Wang H, Coperet C. Machine learning–enabled high-entropy alloy discovery. Science. 2022;378(6615):155-62.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/a122684570-e1b5abec-bb3b-4768-93e0-dc57c68c458b">
					  <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>
						 <doi>10.1038/s41586-018-0337-2</doi> 					</citation>
          					<citation key="rk-10.68159/a122684570-f930fc8c-3059-40e8-b7a1-42b45990710b">
					  <unstructured_citation>Ramprasad R, Batra R, Pilania G, Mann CD, Kumar U. Machine learning in materials informatics: a review. npj Comput Mater. 2017;3(54).</unstructured_citation>
						 <doi>10.1038/s41524-017-0056-5</doi> 					</citation>
          					<citation key="rk-10.68159/a122684570-b3072128-2a6d-4ac3-9400-32db85cbcfef">
					  <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(204).</unstructured_citation>
						 <doi>10.1038/s41524-022-00884-7</doi> 					</citation>
          					<citation key="rk-10.68159/a122684570-3fb5f6eb-f93d-4e5f-aee4-0a124c76f8c0">
					  <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(21).</unstructured_citation>
						 <doi>10.1038/s41524-019-0153-8</doi> 					</citation>
          					<citation key="rk-10.68159/a122684570-6998945f-f1bf-4c6b-8a48-aeb8b2a17751">
					  <unstructured_citation>Xian Y, Dang P, Tian Y, Jiang X, Zhou Y, Ding X, et al. Compositional design of multicomponent alloys using reinforcement learning. Acta Mater. 2024;274(120017).</unstructured_citation>
						 <doi>10.1016/j.actamat.2024.120017</doi> 					</citation>
          					<citation key="rk-10.68159/a122684570-4c17d6fc-3260-42ed-a00e-d0d57ef25473">
					  <unstructured_citation>Tom G, Schmid SP, Baird SG, Cao Y, Darvish K, Hao H, et al. Self-Driving laboratories for chemistry and materials science. Chem Rev. 2024;124(16):9633-732.</unstructured_citation>
						 <doi>10.1021/acs.chemrev.4c00055</doi> 					</citation>
          					<citation key="rk-10.68159/a122684570-3e0d2a31-83df-4c6c-aa2b-ff53f0ef6ba2">
					  <unstructured_citation>Pyzer-Knapp EO, Manica M, Staar P, Morin L, Ruch P, Laino T, et al. Foundation models for materials discovery – current state and future directions. npj Comput Mater. 2025;11(61).</unstructured_citation>
						 <doi>10.1038/s41524-025-01538-0</doi> 					</citation>
          					<citation key="rk-10.68159/a122684570-430861b3-8935-47a2-b7ef-577661469022">
					  <unstructured_citation>Persaud D, Ward L, Hattrick-Simpers J. Reproducibility in materials informatics: Lessons from ‘A general-purpose machine learning framework for predicting properties of inorganic materials’. Digit Discov. 2024;3(3):281-6.</unstructured_citation>
						 <doi>10.1039/D3DD00199G</doi> 					</citation>
          					<citation key="rk-10.68159/a122684570-f4ef357f-63aa-48dd-89ed-4512328c9f48">
					  <unstructured_citation>Tran A, Mitchell JA, Swiler LP, Wildey T. An active learning high-throughput microstructure calibration framework for solving inverse structure–process problems in materials informatics. Acta Mater. 2020;194:80-92.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/a122684570-a6d1ad6d-26d7-43a8-9c5f-91fb4202b876">
					  <unstructured_citation>MacLeod BP, Parlane CFG, McCulloch MD, Lugier R, Schrek B, Dvorak DJ, et al. Autonomous materials synthesis via hierarchical active learning of phase maps. Sci Adv. 2021;7(51):eabg4930.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/a122684570-ba1e550e-66b7-4866-9810-df1d1b40fd9c">
					  <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(5966).</unstructured_citation>
						 <doi>10.1038/s41467-020-19597-w</doi> 					</citation>
          					<citation key="rk-10.68159/a122684570-b9fd9b55-b950-479d-aaf2-0af74f3364e4">
					  <unstructured_citation>Allec SI, Ziatdinov M. Active and transfer learning with partially Bayesian neural networks for materials and chemicals. Digit Discov. 2025;4:1284-97.</unstructured_citation>
						 <doi>10.1039/D5DD00027K</doi> 					</citation>
          					<citation key="rk-10.68159/a122684570-4af1f4be-fcde-416f-a1b7-943e42eebe22">
					  <unstructured_citation>Karpovich C, Pan EY, Olivetti EA. Deep reinforcement learning for inverse inorganic materials design. npj Comput Mater. 2024;10(287).</unstructured_citation>
						 <doi>10.1038/s41524-024-01474-5</doi> 					</citation>
          					<citation key="rk-10.68159/a122684570-5d2670c3-5454-4cfb-963d-dc79dd9b9fc8">
					  <unstructured_citation>Wang C, Takeuchi I, Liu H, Yu H, Kusne AG, Zhang J-C, et al. Real-time experiment-theory closed-loop interaction for autonomous materials science. Sci Adv. 2025;11(27):eadu7426.</unstructured_citation>
						 <doi>10.1126/sciadv.adu7426</doi> 					</citation>
          					<citation key="rk-10.68159/a122684570-65cca587-08b1-407a-a5d1-5893964b52e3">
					  <unstructured_citation>Xian Y, Ding X, Jiang X, Zhou Y, Sun J, Xue D, et al. Unlocking the black box beyond Bayesian global optimization for materials design using reinforcement learning. npj Comput Mater. 2025;11(143).</unstructured_citation>
						 <doi>10.1038/s41524-025-01639-w</doi> 					</citation>
          				</citation_list>
			</journal_article>
		</journal>
	</body>
</doi_batch>
