<?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-HYMP-1791510529-k354459599</doi_batch_id>
		<timestamp>1791510529</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>Algorithmic Simplicity as Scientific Virtue: A Conceptual Tension in Materials AI Design</title>
				</titles>
								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>Siti</given_name>
            <surname>Rahman</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Ahmad</given_name>
            <surname>Zaki</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Nurul</given_name>
            <surname>Huda</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Amir</given_name>
            <surname>Faisal</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Lim</given_name>
            <surname>Wei</surname>
					</person_name>
          				</contributors>
								<publication_date>
					<year>2025</year>
				</publication_date>
				<doi_data>
					<doi>10.68159/k354459599</doi>
					<resource>https://iamrp.net/pub/journal/1/article/k354459599</resource>
				</doi_data>
				<citation_list>
          					<citation key="rk-10.68159/k354459599-bb088c47-2b66-483a-afec-bf0f84d2f97a">
					  <unstructured_citation>Gross F. Occam’s razor in molecular and systems biology. Philos Sci. 2019;86(5):1134-45.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k354459599-db9b9e36-8319-452a-94c5-c784f5b56ccf">
					  <unstructured_citation>Hanin B, Zlokapa A. Bayesian interpolation with deep linear networks. Proc Natl Acad Sci U S A. 2023;120(23):e2301345120.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k354459599-193d828b-2687-4a7e-b416-e5a6db6ce3f8">
					  <unstructured_citation>Hansen KB. The virtue of simplicity: On machine learning models in algorithmic trading. Big Data Soc. 2020;7(1):2053951720926558.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k354459599-bfd8653c-0b76-418b-b6c7-71a2718245e5">
					  <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/k354459599-ce6baa88-777a-4fb8-8be0-8b84c33d8a7e">
					  <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/k354459599-d43bf2c8-b866-42c3-bb98-8b9093b7daae">
					  <unstructured_citation>Chen C, Ye W, Zuo Y, Zheng C, Ong SP. Graph networks as a universal machine learning framework for molecules and crystals. Chem Mater. 2019;31(9):3564-72.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k354459599-1726ecc8-d1a6-4904-a1ef-cee12d5ea04a">
					  <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/k354459599-02b2e89e-5d7a-4996-8015-33c455a47f7a">
					  <unstructured_citation>Zhong X, Gallagher B, Liu S, Kailkhura B, Hiszpanski A, Han TY. Explainable machine learning in materials science. npj Comput Mater. 2022;8(1):204.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k354459599-383b7823-4c35-4fb9-81d0-cee4f53eebac">
					  <unstructured_citation>Vasudevan RK, Ziatdinov M, Vlcek L, Kalinin SV. Off-the-shelf deep learning is not enough, and requires parsimony, Bayesianity, and causality. npj Comput Mater. 2021;7(1):16.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k354459599-b166ce79-aa6d-4421-ae7e-79457181bb6c">
					  <unstructured_citation>Lu Z, Chen X, Liu X, Lin D, Wu Y, Zhang Y, et al. Interpretable machine-learning strategy for soft-magnetic property and thermal stability in Fe-based metallic glasses. npj Comput Mater. 2020;6(1):187.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k354459599-92a47577-bcde-4565-be01-d2078fb470f6">
					  <unstructured_citation>Oviedo F, Ferres JL, Buonassisi T, Butler KT. Interpretable and explainable machine learning for materials science and chemistry. Acc Mater Res. 2022;3(6):597-607.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k354459599-a99ac95a-ca31-43e4-bc03-a146fbbf4273">
					  <unstructured_citation>Desai S, Strachan A. Parsimonious neural networks learn interpretable physical laws. Sci Rep. 2021;11(1):12761.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k354459599-446e6bd1-621e-4faa-a3b0-029d23411d70">
					  <unstructured_citation>Gallegos M, Vassilev-Galindo V, Poltavsky I, Martín Pendás Á, Tkatchenko A. Explainable chemical artificial intelligence from accurate machine learning of real-space chemical descriptors. Nat Commun. 2024;15(1):4345.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k354459599-3efef44b-7931-4bf6-8b0c-928d816deaa0">
					  <unstructured_citation>Zhou Z, Zhou Y, He Q, Ding Z, Li F, Yang Y. Machine learning guided appraisal and exploration of phase design for high entropy alloys. npj Comput Mater. 2019;5(1):128.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k354459599-5254bc01-8557-4ccc-8da5-35603767af56">
					  <unstructured_citation>Singh C, Askari A, Caruana R, Gao J. Augmenting interpretable models with large language models during training. Nat Commun. 2023;14(1):7913.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k354459599-6c4b2587-a43c-4193-80c8-c96d49456268">
					  <unstructured_citation>Moitzi F, Romaner L, Ruban AV, Hodapp M, Peil OE. Ab initio framework for deciphering trade-off relationships in multi-component alloys. npj Comput Mater. 2024;10(1):152.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k354459599-a57988a8-64c7-4157-b2e6-ffab650e26d0">
					  <unstructured_citation>Kim N, Lee D, Kim C, Lee D, Hong Y. Simple arithmetic operation in latent space can generate a novel three-dimensional graph metamaterials. npj Comput Mater. 2024;10(1):236.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k354459599-c55e9284-016e-4f4a-bee9-06e251d19805">
					  <unstructured_citation>La Cava WG, Lee PC, Ajmal I, Ding X, Solanki P, Cohen JB, et al. A flexible symbolic regression method for constructing interpretable clinical prediction models. NPJ Digit Med. 2023;6(1):107.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k354459599-3ffc07d2-ff2a-41d0-93d6-589aa1a450d7">
					  <unstructured_citation>Korolev V, Protsenko P. Accurate, interpretable predictions of materials properties within transformer language models. Patterns. 2023;4(10):100803.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k354459599-44acaf29-1f07-4fde-90d1-edba47960d39">
					  <unstructured_citation>Bell A, Solano-Kamaiko I, Nov O, Stoyanovich J. It’s just not that simple: An empirical study of the accuracy-explainability trade-off in machine learning for public policy. In: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency. New York, NY: ACM; 2022. pp. 248-66.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k354459599-a623d8d8-48e0-4745-882b-c4387d947500">
					  <unstructured_citation>Dziugaite GK, Ben-David S, Roy DM. Enforcing interpretability and its statistical impacts: Trade-offs between accuracy and interpretability. arXiv preprint arXiv:2010.13764. 2020 Oct 26.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k354459599-5462441a-55c3-43a0-a742-b2280e0032e9">
					  <unstructured_citation>Linardatos P, Papastefanopoulos V, Kotsiantis S. Explainable AI: A review of machine learning interpretability methods. Entropy. 2020;23(1):18.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k354459599-0e41be18-73a6-4984-9d9d-10f1c6fef0c4">
					  <unstructured_citation>Sullivan E. Understanding from machine learning models. Br J Philos Sci. 2022;73(1):109-33.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k354459599-e1ea08ad-7bb7-4148-b4f5-cdf710faf1d1">
					  <unstructured_citation>Damewood J, Karaguesian J, Lunger JR, Tan AR, Xie M, Peng J, et al. Representations of materials for machine learning. Annu Rev Mater Res. 2023;53(1):399-426.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k354459599-70c49570-7283-4dd4-a260-5284c24c8f4f">
					  <unstructured_citation>Liu Y, Jovanovic M, Mallayya K, Maddox WJ, Wilson AG, Klemenz S, et al. Materials expert-artificial intelligence for materials discovery. Commun Mater. 2025;6(1):212.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k354459599-b1c11561-58ee-42cd-bdce-48f2042a6e63">
					  <unstructured_citation>Belle V, Papantonis I. Principles and practice of explainable machine learning. Front Big Data. 2021;4:688969.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k354459599-5f2d9998-3ade-4dc6-aefd-2df752b5fc93">
					  <unstructured_citation>Bengio Y, Goodfellow I, Courville A. Deep learning. Cambridge, MA, USA: MIT Press; 2017.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k354459599-de7759f0-fb8f-43ad-809b-940881d20945">
					  <unstructured_citation>Gubernatis JE, Lookman TJ. Machine learning in materials design and discovery: Examples from the present and suggestions for the future. Phys Rev Mater. 2018;2(12):120301.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k354459599-4895a94d-942b-46c0-a1af-f0aabfa85fba">
					  <unstructured_citation>Silveyra JM, Ferrara E, Huber DL, Monson TC. Soft magnetic materials for a sustainable and electrified world. Science. 2018;362(6413):eaao0195.</unstructured_citation>
											</citation>
          				</citation_list>
			</journal_article>
		</journal>
	</body>
</doi_batch>
