<?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-RrmC-1792527683-g153400470</doi_batch_id>
		<timestamp>1792527683</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 Artificial Intelligence for Materials Science</full_title>
				<abbrev_title>J. Artif. Intell. Mater. Sci.</abbrev_title>
				<issn>3149-8957</issn>
			</journal_metadata>
			<journal_issue>
				<publication_date>
					<year>2025</year>
				</publication_date>
				<journal_volume>
					<volume>4</volume>
				</journal_volume>
				<issue>1</issue>
			</journal_issue>
			<journal_article publication_type="full_text">
				<titles>
					<title>Conceptual Models of the AI-Materials Scientist Interface — From Tool to Collaborator: A Review Study</title>
				</titles>
								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>Elena</given_name>
            <surname>Petrova</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Ivan</given_name>
            <surname>Georgiev</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Nikolay</given_name>
            <surname>Stoyanov</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Petar</given_name>
            <surname>Kolev</surname>
					</person_name>
          				</contributors>
								<publication_date>
					<year>2025</year>
				</publication_date>
				<doi_data>
					<doi>10.68159/g153400470</doi>
					<resource>https://iamrp.net/pub/journal/1/article/g153400470</resource>
				</doi_data>
				<citation_list>
          					<citation key="rk-10.68159/g153400470-da365512-25df-430b-8d49-06c4013a3cc5">
					  <unstructured_citation>Kim CM, Nam TJ. Exploration on everyday objects as an IoT control interface. In: Proceedings of the 2022 ACM Designing Interactive Systems Conference. New York, NY: ACM; 2022. p. 1654-68.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g153400470-160b2207-cf28-4fb0-a319-6035fc79e728">
					  <unstructured_citation>Mitchell M. Artificial intelligence: A guide for thinking humans. New York, NY: Farrar, Straus and Giroux; 2019.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g153400470-cb3984b2-5829-440b-8a8c-35112d472acc">
					  <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/g153400470-665955c3-3c9d-4d38-96b1-173ece8e32fa">
					  <unstructured_citation>Schmidt J, Marques MR, Botti S, Marques MA. Recent advances and applications of machine learning in solid-state materials science. npj Comput Mater. 2019;5(1):83.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g153400470-d0d03ee6-dafb-4a27-ab5f-2650113ecd05">
					  <unstructured_citation>Zunger A. Inverse design in search of materials with target functionalities. Nat Rev Chem. 2018;2(4):0121.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g153400470-b9a32908-b42b-4f80-ba9f-e93c4d55078d">
					  <unstructured_citation>Häse F, Roch LM, Aspuru-Guzik A. Next-generation experimentation with self-driving laboratories. Trends Chem. 2019;1(3):282-91.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g153400470-960a40e0-edff-4809-99e4-76060d76ac24">
					  <unstructured_citation>MacLeod BP, Parlane FG, Morrissey TD, Häse F, Roch LM, Dettelbach KE, et al. Self-driving laboratory for accelerated discovery of thin-film materials. Sci Adv. 2020;6(20):eaaz8867.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g153400470-1115c05e-5ed7-4845-9a41-9689a6d08d5c">
					  <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/g153400470-3ecb3c52-26dc-4666-b72f-2c19d59dddec">
					  <unstructured_citation>Otyepka M, Pykal M, Otyepka M. Advancing materials discovery through artificial intelligence. Appl Mater Today. 2025;47:102981.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g153400470-3432fbb5-1043-4b7f-9641-e7bac2868018">
					  <unstructured_citation>Montoya JH, Aykol M, Anapolsky A, Gopal CB, Herring PK, Hummelshøj JS, et al. Toward autonomous materials research: Recent progress and future challenges. Appl Phys Rev. 2022;9(1):011405.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g153400470-922d80d0-e48a-4621-9486-09c0ff05eafb">
					  <unstructured_citation>Kitano H. Nobel turing challenge: Creating the engine for scientific discovery. npj Syst Biol Appl. 2021;7(1):29.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g153400470-cf81f634-82fc-4c0d-a1bb-cbdec08b91f5">
					  <unstructured_citation>Karpatne A, Deshwal A, Jia X, Ding W, Steinbach M, Zhang A, et al. AI-enabled scientific revolution in the age of generative AI: Second NSF workshop report. npj Artif Intell. 2025;1(1):18.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g153400470-227552e7-39ae-4388-a44e-45ecfbe5ed75">
					  <unstructured_citation>Shao E, Wang Y, Qian Y, Pan Z, Liu H, Wang D. SciSciGPT: Advancing human-AI collaboration in the science of science. Nat Comput Sci. 2025:1-5.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g153400470-4b99130d-3d1b-4a72-ae81-45fef4935663">
					  <unstructured_citation>O’Neill T, McNeese N, Barron A, Schelble B. Human–autonomy teaming: A review and analysis of the empirical literature. Hum Factors. 2022;64(5):904-38.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g153400470-4f3e4a7e-0e0f-4a6c-bdbc-b44165c90458">
					  <unstructured_citation>O’Neill TA, Flathmann C, McNeese NJ, Salas E. Human-autonomy teaming: Need for a guiding team-based framework? Comput Hum Behav. 2023;146:107762.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g153400470-33086d82-1eae-4916-a513-34785a0c9f8a">
					  <unstructured_citation>McNeese NJ, Flathmann C, O’Neill TA, Salas E. Stepping out of the shadow of human-human teaming: Crafting a unique identity for human-autonomy teams. Comput Hum Behav. 2023;148:107874.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g153400470-59f74211-8961-41d7-88c9-04d7603c2f22">
					  <unstructured_citation>Andrews RW, Lilly JM, Srivastava D, Feigh KM. The role of shared mental models in human-AI teams: A theoretical review. Theor Issues Ergon Sci. 2023;24(2):129-75.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g153400470-0588a563-9d4a-4b87-bcb0-5605ebafecd4">
					  <unstructured_citation>Bansal G, Nushi B, Kamar E, Lasecki WS, Weld DS, Horvitz E. Beyond accuracy: The role of mental models in human-AI team performance. In: Proceedings of the AAAI Conference on Human Computation and Crowdsourcing. Palo Alto, CA: AAAI Press; 2019;7(1):2-11.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g153400470-07dbedc5-4676-48a8-860d-957807078a9e">
					  <unstructured_citation>Bansal G, Nushi B, Kamar E, Horvitz E, Weld DS. Is the most accurate AI the best teammate? Optimizing AI for teamwork. In: Proceedings of the AAAI Conference on Artificial Intelligence. Palo Alto, CA: AAAI Press; 2021;35(13):11405-14.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g153400470-7c3a2348-5007-4fca-9b32-d16efd521661">
					  <unstructured_citation>Bansal G, Wu T, Zhou J, Fok R, Nushi B, Kamar E, et al. Does the whole exceed its parts? The effect of AI explanations on complementary team performance. In: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. New York, NY: ACM; 2021. p. 1-16.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g153400470-7cfbd141-f5d6-4ca7-807b-8c331f191963">
					  <unstructured_citation>Hemmer P, Schemmer M, Kühl N, Vössing M, Satzger G. Complementarity in human-AI collaboration: Concept, sources, and evidence. Eur J Inf Syst. 2025;34(6):979-1002.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g153400470-7b461531-ace0-43aa-b9fa-d76ff1795ea2">
					  <unstructured_citation>Senoner J, Schallmoser S, Kratzwald B, Feuerriegel S, Netland T. Explainable AI improves task performance in human–AI collaboration. Sci Rep. 2024;14(1):31150.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g153400470-b98e66b2-bb83-4772-a53a-69ab77e82393">
					  <unstructured_citation>Wu S, Liu Y, Ruan M, Chen S, Xie XY. Human-generative AI collaboration enhances task performance but undermines human’s intrinsic motivation. Sci Rep. 2025;15(1):15105.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g153400470-e73a829c-35c9-442e-a5ab-0eb65eeb1d37">
					  <unstructured_citation>Wang Z, Cao L, Jin Q, Chan J, Wan N, Afzali B, et al. A foundation model for human-AI collaboration in medical literature mining. Nat Commun. 2025;16(1):8361.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g153400470-444a3492-c7d4-49ff-9430-9098c557a04e">
					  <unstructured_citation>Zhang G, Chong L, Kotovsky K, Cagan J. Trust in an AI versus a human teammate: The effects of teammate identity and performance on human-AI cooperation. Comput Hum Behav. 2023;139:107536.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g153400470-341f9949-1a84-4cb7-89f1-16f29dfbc02b">
					  <unstructured_citation>Flathmann C, Schelble BG, Rosopa PJ, McNeese NJ, Mallick R, Madathil KC. Examining the impact of varying levels of AI teammate influence on human-AI teams. Int J Hum Comput Stud. 2023;177:103061.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g153400470-e29ed4b4-392d-4fb8-826a-5eb40f54ebde">
					  <unstructured_citation>Xu W, Gao Z. Applying HCAI in developing effective human-AI teaming: A perspective from human-AI joint cognitive systems. Interactions. 2024;31(1):32-7.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g153400470-0e08e40b-3601-4457-96bc-2a0840f58500">
					  <unstructured_citation>Caldwell S, Sweetser P, O’donnell N, Knight MJ, Aitchison M, Gedeon T, et al. An agile new research framework for hybrid human-AI teaming: Trust, transparency, and transferability. ACM Trans Interact Intell Syst. 2022;12(3):1-36.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g153400470-90036324-ae3e-4f35-8e01-f163965dbedb">
					  <unstructured_citation>Zhang R, McNeese NJ, Freeman G, Musick G. An ideal human expectations of AI teammates in human-AI teaming. Proc ACM Hum Comput Interact. 2021;4(CSCW3):1-25.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g153400470-41a03030-7b15-4c8f-9415-22ee3c792c2c">
					  <unstructured_citation>Attig C, Wollstadt P, Schrills T, Franke T, Wiebel-Herboth CB. More than task performance: Developing new criteria for successful human-AI teaming using the cooperative card game hanabi. In: Extended Abstracts of the CHI Conference on Human Factors in Computing Systems. New York, NY: ACM; 2024. p. 1-11.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g153400470-880ff06f-a35f-4c57-8a58-0d67ef814074">
					  <unstructured_citation>Duan W, Flathmann C, McNeese N, Scalia MJ, Zhang R, Gorman J, et al. Trusting autonomous teammates in human-AI teams-a literature review. In: Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems. New York, NY: ACM; 2025. p. 1-23.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g153400470-2330cf3e-d39c-49d2-a65f-cac24e2a4e7c">
					  <unstructured_citation>Hu M, Zhang G, Chong L, Cagan J, Goucher-Lambert K. How being outvoted by AI teammates impacts human-AI collaboration. Int J Hum Comput Interact. 2025;41(7):4049-66.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g153400470-843d23fe-52ef-45d3-bc32-860806fe9966">
					  <unstructured_citation>Winter J. AI teammates and human performance: Evidence for commitment deficits. Comput Hum Behav Rep. 2025;20:100828.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g153400470-76199fb8-dbe7-4586-ab95-5723fd04e080">
					  <unstructured_citation>Breckner K, Neumayr T, Streit M, Augstein M. Personalized complementarity in human-AI collaboration. Bonn: Gesellschaft für Informatik eV; 2024. p. 10-18420.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g153400470-ae666dc4-eb29-4684-a59c-7df8c7548214">
					  <unstructured_citation>Rodani T. Reliable AI in material science: A fair-by-design path from data to services. Available from: https://ricerca.unityfvg.it/entities/publication/88217b2b-b426-41d5-ac14-2d970f8877fa/details</unstructured_citation>
											</citation>
          				</citation_list>
			</journal_article>
		</journal>
	</body>
</doi_batch>
