<?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-JPGd-1791561621-n442698587</doi_batch_id>
		<timestamp>1791561621</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>When Accuracy Is Not Enough: A Decision-Theoretic Framework for Evaluating Materials AI Models</title>
				</titles>
								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>Li</given_name>
            <surname>Zhang</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Wei</given_name>
            <surname>Chen</surname>
					</person_name>
          				</contributors>
								<publication_date>
					<year>2025</year>
				</publication_date>
				<doi_data>
					<doi>10.68159/n442698587</doi>
					<resource>https://iamrp.net/pub/journal/1/article/n442698587</resource>
				</doi_data>
				<citation_list>
          					<citation key="rk-10.68159/n442698587-646470cf-5a18-412b-be2d-873a7bc729bc">
					  <unstructured_citation>Merchant A, Batzner S, Schoenholz SS, Aykol M, Cheon G, Cubuk ED. Scaling deep learning for materials discovery. Nature. 2023;624(7990):80-5.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/n442698587-1e2534b0-3562-4394-b5e3-44f8a15b6df8">
					  <unstructured_citation>Wang H, Fu T, Du Y, Gao W, Huang K, Liu Z, et al. Scientific discovery in the age of artificial intelligence. Nature. 2023;620(7972):47-60.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/n442698587-0cb25476-2b49-4ab5-b7d7-9b8f266db5d0">
					  <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/n442698587-be9f8ad7-99a2-4aee-8fa4-a8bd084bd3d7">
					  <unstructured_citation>Fung V, Hu G, Ganesh P, Sumpter BG. Machine learned features from density of states for accurate adsorption energy prediction. Nat Commun. 2021;12(1):88.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/n442698587-e4c21a3a-32d0-4ffc-b406-3353bc90fb63">
					  <unstructured_citation>Schleder GR, Focassio B, Fazzio A. Machine learning for materials discovery: Two-dimensional topological insulators. Appl Phys Rev. 2021;8(3):031409.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/n442698587-0896f674-8cb4-44ba-958a-d2458b84977a">
					  <unstructured_citation>Zhou Q, Chen X, Wang J. Machine learning assisted material discovery: A small data approach. Acc Mater Res. 2024;5(5):571-84.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/n442698587-5b88e272-2ab3-4edc-ac25-cbe30fe0bf10">
					  <unstructured_citation>Kim KS. Machine learning for accelerating energy materials discovery: Bridging quantum accuracy with computational efficiency. Adv Energy Mater. 2024;14(40):2403356.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/n442698587-9c5a97ef-28a7-456f-b4ca-6b3affc66fd4">
					  <unstructured_citation>Mohammadiun S, Hu G, Gharahbagh AA, Li J, Hewage K, Sadiq R. Evaluation of machine learning techniques to select marine oil spill response methods under small-sized dataset conditions. J Hazard Mater. 2022;436:129282.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/n442698587-5902a036-4271-440c-82a3-42f6dbbbc6e0">
					  <unstructured_citation>Harimi A, Majd Y, Gharahbagh AA, Hajihashemi V, Esmaileyan Z, Machado JJ, et al. Classification of heart sounds using chaogram transform and deep convolutional neural network transfer learning. Sensors (Basel). 2022;22(24):9569.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/n442698587-77c797bf-f3e8-41b4-8443-b907eb719268">
					  <unstructured_citation>Kanase-Patil AB, Kaldate AP, Lokhande SD, Panchal H, Suresh M, Priya V. A review of artificial intelligence-based optimization techniques for the sizing of integrated renewable energy systems in smart cities. Environ Technol Rev. 2020;9(1):111-36.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/n442698587-f9c8b407-5386-4c67-b897-efecf0e9e800">
					  <unstructured_citation>Krishnan NA, Kodamana H, Bhattoo R. Machine learning for materials discovery: Numerical recipes and practical applications. Cham: Springer International Publishing; 2024.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/n442698587-24c5350b-eea2-49be-b1a4-49f5abe81924">
					  <unstructured_citation>Guo Z, Wu Y, Hartline JD, Hullman J. A decision theoretic framework for measuring ai reliance. In: Proceedings of the 2024 acm conference on fairness, accountability, and transparency. 2024. p. 221-36.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/n442698587-60417187-56de-43d2-b6ac-5c8ae8bd932f">
					  <unstructured_citation>BaniHani I, Alawadi S, Elmrayyan N. Ai and the decision-making process: A literature review in healthcare, financial, and technology sectors. J Decis Syst. 2024;33(sup1):389-99.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/n442698587-8fa9f404-06f3-4dd8-9142-8eae00a471f6">
					  <unstructured_citation>Ben-Michael E, Greiner DJ, Huang M, Imai K, Jiang Z, Shin S. Does ai help humans make better decisions? A methodological framework for experimental evaluation. arXiv. 2024;arXiv:2403.12108.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/n442698587-abfab024-ec72-441f-b4ae-b53d267853a9">
					  <unstructured_citation>Peterson JC, Bourgin DD, Agrawal M, Reichman D, Griffiths TL. Using large-scale experiments and machine learning to discover theories of human decision-making. Science. 2021;372(6547):1209-14.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/n442698587-3709089b-16c1-4254-8f60-69a6ca4743cb">
					  <unstructured_citation>Tolmeijer S, Christen M, Kandul S, Kneer M, Bernstein A. Capable but amoral? Comparing ai and human expert collaboration in ethical decision making. In: Proceedings of the 2022 chi conference on human factors in computing systems. 2022. p. 1-17.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/n442698587-e5e27fad-1bb2-482a-955b-e0c8d9c89e9f">
					  <unstructured_citation>Mohammadiun S, Hu G, Gharahbagh AA, Li J, Hewage K, Sadiq R. Intelligent computational techniques in marine oil spill management: A critical review. J Hazard Mater. 2021;419:126425.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/n442698587-65289c6e-b1c7-4b38-9c05-bc44b4f4773b">
					  <unstructured_citation>von Lilienfeld OA, Müller KR, Tkatchenko A. Exploring chemical compound space with quantum-based machine learning. Nat Rev Chem. 2020;4(7):347-58.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/n442698587-8ab0dfbc-ed93-4dc1-a14b-6f628a315b54">
					  <unstructured_citation>Schuurman Y, Goulart de Araujo L, Vilcocq L, Fongarland P. Recent developments in the use of machine learning in catalysis kinetics. Catal Today. 2021;369:3-12.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/n442698587-72401ee8-c09d-4d21-918e-8bfeeed262a1">
					  <unstructured_citation>Noack MM, Doerk GS, Li R, Streit JK, Vaia RA, Yager KG, et al. Autonomous materials discovery driven by gaussian process regression with inhomogeneous measurement noise and anisotropic kernels. Sci Rep. 2020;10(1):17663.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/n442698587-539c09d4-6861-460f-9dca-bdc3c19b9b07">
					  <unstructured_citation>Dobrzański LA, Honysz R. Artificial intelligence and virtual environment application for materials design methodology. Arch Mater Sci Eng. 2010;45(2):69-94.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/n442698587-de723f55-8cca-4c4d-9dd1-a23b02a0cf3a">
					  <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>
											</citation>
          					<citation key="rk-10.68159/n442698587-5d260147-784f-4320-ae58-a9db00e27f5a">
					  <unstructured_citation>Li S, You F. Genai for scientific discovery in electrochemical energy storage: State-of-the-art and perspectives from nano- and micro-scale. Small. 2024;20(50):2406153.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/n442698587-29a17f37-c178-4a32-83d0-0270652a8c78">
					  <unstructured_citation>Li DZ, Chen L, Liu G, Yuan ZY, Li BF, Zhang X, et al. Porous metal–organic frameworks for methane storage and capture: Status and challenges. New Carbon Mater. 2021;36(3):468-96.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/n442698587-e3b0046e-5f8b-41c3-9a27-11a3d2807d89">
					  <unstructured_citation>Cai J, Chu X, Xu K, Li H, Wei J. Machine learning-driven new material discovery. Nanoscale Adv. 2020;2(8):3115-30.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/n442698587-2fe9e327-8bdf-415f-b48f-186396605a54">
					  <unstructured_citation>Dou B, Zhu Z, Merkurjev E, Ke L, Chen L, Jiang J, et al. Machine learning methods for small data challenges in molecular science. Chem Rev. 2023;123(13):8736-80.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/n442698587-3cea1b80-2ff4-40a1-8906-4a78da9e58c8">
					  <unstructured_citation>Huang JS, Liew KM, Ademiloye A. Artificial intelligence in materials modeling and design. Arch Comput Methods Eng. 2021;28(5):3399-413.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/n442698587-72fda936-0226-48c6-b30f-b5d4f12410ed">
					  <unstructured_citation>Hirschfeld L, Swanson K, Yang K, Barzilay R, Coley CW. Uncertainty quantification using neural networks for molecular property prediction. J Chem Inf Model. 2020;60(8):3770-80.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/n442698587-3f70f0fa-59e9-4f3f-995e-4d9cfd1027fb">
					  <unstructured_citation>Guo K, Yang Z, Yu CH, Buehler MJ. Artificial intelligence and machine learning in design of mechanical materials. Mater Horiz. 2021;8(4):1153-72.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/n442698587-522154e2-e70f-4806-9013-c060f68499e3">
					  <unstructured_citation>Lu B, Xia Y, Ren Y, Xie M, Zhou L, Vinai G, et al. When machine learning meets 2d materials: A review. Adv Sci. 2024;11(13):2305277.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/n442698587-35561586-8ce3-4cc6-a89b-7484c5c56973">
					  <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/n442698587-7c850ae4-43b0-4709-90cd-6077d90c4ca0">
					  <unstructured_citation>Hao WJ, Tasir Z. Development of a theoretical framework of moocs with gamification elements to enhance students’ higher-order thinking skills: A critical review of the literature. J Inf Technol Educ Res. 2024;23:1-25.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/n442698587-fe34df09-76c5-4338-bcec-2248a0f41e4b">
					  <unstructured_citation>Benotsmane R, Dudás L, Kovács G. Survey on artificial intelligence algorithms used in industrial robotics. Multidiszciplináris Tudományok. 2020;10(4):194-205.</unstructured_citation>
											</citation>
          				</citation_list>
			</journal_article>
		</journal>
	</body>
</doi_batch>
