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			<depositor_name>Institute for Advanced Materials Research Press</depositor_name>
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				<full_title>Journal of Artificial Intelligence for Materials Science</full_title>
				<abbrev_title>J. Artif. Intell. Mater. Sci.</abbrev_title>
				<issn>3149-8957</issn>
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					<year>2025</year>
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					<volume>4</volume>
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				<issue>1</issue>
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					<title>Learning Under Scarcity: A Conceptual Theory of Small-Data Regimes in Materials Artificial Intelligence</title>
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								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>Oliver</given_name>
            <surname>Grant</surname>
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            <given_name>Daniel</given_name>
            <surname>Brooks</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Amelia</given_name>
            <surname>Carter</surname>
					</person_name>
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								<publication_date>
					<year>2025</year>
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					  <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>
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					  <unstructured_citation>Cai R, Han T, Liao W, Huang J, Li D, Kumar A, et al. Prediction of surface chloride concentration of marine concrete using ensemble machine learning. Cem Concr Res. 2020;136:106164.</unstructured_citation>
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          					<citation key="rk-10.68159/a094051263-f7799df8-a3d2-4e8d-82ea-26915c601b0d">
					  <unstructured_citation>Han T, Siddique A, Khayat K, Huang J, Kumar A. An ensemble machine learning approach for prediction and optimization of modulus of elasticity of recycled aggregate concrete. Constr Build Mater. 2020;244:118271.</unstructured_citation>
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          					<citation key="rk-10.68159/a094051263-de31a6a6-8c41-4f8b-a634-ca27a113fc51">
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					  <unstructured_citation>Li S, Nakata A. CSIML: a cost-sensitive and iterative machine-learning method for small and imbalanced materials data sets. Chem Lett. 2024;53(5):upae090.</unstructured_citation>
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					  <unstructured_citation>Karpovich C, Pan E, Jensen Z, Olivetti E. Interpretable machine learning enabled inorganic reaction classification and synthesis condition prediction. Chem Mater. 2023;35(2):734-45.</unstructured_citation>
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					  <unstructured_citation>Tian SIP, Walsh A, Ren Z, Li Q, Buonassisi T. What information is necessary and sufficient to predict materials properties using machine learning? ACS Appl Mater Interfaces. 2022;14(45):50985-95.</unstructured_citation>
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					  <unstructured_citation>Chen X, Lu S, Wan X, Chen Q, Zhou Q, Jiang J. Accurate property prediction with interpretable machine learning model for small datasets via transformed atom vector. Comput Mater Sci. 2023;218:111949.</unstructured_citation>
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					  <unstructured_citation>Omee SS. Scalable deep learning framework for materials discovery: MaterialsAtlas.org. Machine Learning: Science and Technology. 2023;4(1):015001.</unstructured_citation>
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					  <unstructured_citation>Lambard G. Machine learning in materials science. 2020.</unstructured_citation>
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