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			<depositor_name>Institute for Advanced Materials Research Press</depositor_name>
			<email_address>info@iamrp.net</email_address>
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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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			<journal_issue>
				<publication_date>
					<year>2023</year>
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					<volume>2</volume>
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				<issue>1</issue>
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				<titles>
					<title>Representation Compression and Scientific Loss in Materials AI</title>
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          					<person_name sequence="first" contributor_role="author">
            <given_name>Claire</given_name>
            <surname>Dupont</surname>
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            <given_name>Julien</given_name>
            <surname>Martin</surname>
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								<publication_date>
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          					<citation key="rk-10.68159/n403891302-bea261d1-0bf9-431b-944c-17d4a1f17acd">
					  <unstructured_citation>Choudhary K, DeCost B, Chen C, Jain A. Recent advances and applications of deep learning methods in materials science. npj Comput Mater. 2022;8(1):59.</unstructured_citation>
						 <doi>10.1038/s41524-022-00734-6</doi> 					</citation>
          					<citation key="rk-10.68159/n403891302-4ce86bcf-54f4-4ffe-abc0-7b2a28e1abf5">
					  <unstructured_citation>Bai X, Zhang X. Artificial intelligence-powered materials science. Nano Micro Lett. 2025;17(1):1-25.</unstructured_citation>
						 <doi>10.1007/s40820-024-01634-8</doi> 					</citation>
          					<citation key="rk-10.68159/n403891302-288a2398-7f12-4a77-adc3-75b5f19b3c13">
					  <unstructured_citation>Chávez-Angel E, Eriksen MB. Applied artificial intelligence in materials science and material design. Adv Intell Syst. 2025;7(3):2400986.</unstructured_citation>
						 <doi>10.1002/aisy.202400986</doi> 					</citation>
          					<citation key="rk-10.68159/n403891302-d924e066-6997-43b4-8725-583e551c35b0">
					  <unstructured_citation>DeCost BL, Hattrick-Simpers JR, Trautt Z. Scientific AI in materials science: A path to a sustainable and scalable paradigm. Mach Learn Sci Technol. 2020;1(3):031001.</unstructured_citation>
						 <doi>10.1088/2632-2153/ab9a20</doi> 					</citation>
          					<citation key="rk-10.68159/n403891302-23b4fff3-acb9-45d7-bb35-89cf645b9ecf">
					  <unstructured_citation>Damewood J, Karaguesian J, Lunger JR. Representations of materials for machine learning. Annu Rev Mater Res. 2023;53:1-28.</unstructured_citation>
						 <doi>10.1146/annurev-matsci-080921-085947</doi> 					</citation>
          					<citation key="rk-10.68159/n403891302-c3ae8b7e-2818-4ac9-a38a-389759e7d041">
					  <unstructured_citation>Lew AJ, Jin K, Buehler MJ. Designing architected materials for mechanical compression via simulation, deep learning, and experimentation. npj Comput Mater. 2023;9(1):1-12.</unstructured_citation>
						 <doi>10.1038/s41524-023-01036-1</doi> 					</citation>
          					<citation key="rk-10.68159/n403891302-633a1412-5863-479f-8705-a7a9f1c9644b">
					  <unstructured_citation>Unni R, Zhou M, Wiecha PR, Zheng Y. Advancing materials science through next-generation machine learning. J Solid State Mater Sci. 2024;10:100023.</unstructured_citation>
						 <doi>10.1016/j.jssms.2024.100023</doi> 					</citation>
          					<citation key="rk-10.68159/n403891302-c3421943-1bc1-48f2-999c-04259aaa28c8">
					  <unstructured_citation>Olfatbakhsh T, Andrews JL, Milani AS. Materials informatics of woven fabric composites: Effect of different dimensionality reduction and learning methods. Mater Today Commun. 2022;32:103825.</unstructured_citation>
						 <doi>10.1016/j.mtcomm.2022.103825</doi> 					</citation>
          					<citation key="rk-10.68159/n403891302-14cf977b-cf80-489a-9866-e580e42da2fe">
					  <unstructured_citation>Li M, Zhang H, Li S, Zhu W, Ke Y. Machine learning and materials informatics approaches for predicting transverse mechanical properties of unidirectional CFRP composites with microvoids. Mater Des. 2022;220:110962.</unstructured_citation>
						 <doi>10.1016/j.matdes.2022.110962</doi> 					</citation>
          					<citation key="rk-10.68159/n403891302-e9bd245f-9096-4cea-b5a1-11502295cb7e">
					  <unstructured_citation>Wang ZL, Ogawa T, Adachi Y. A machine learning tool for materials informatics. Adv Theory Simul. 2020;3(4):1900177.</unstructured_citation>
						 <doi>10.1002/adts.201900177</doi> 					</citation>
          					<citation key="rk-10.68159/n403891302-5cdc5982-48e2-4276-9e1b-14dac10a3e45">
					  <unstructured_citation>Jha D, Gupta V, Ward L, Yang Z, Wolverton C. Enabling deeper learning on big data for materials informatics applications. Sci Rep. 2021;11(1):14193.</unstructured_citation>
						 <doi>10.1038/s41598-021-83193-1</doi> 					</citation>
          					<citation key="rk-10.68159/n403891302-2ce18dc1-ba5f-4ab6-ada5-25834ddbad7a">
					  <unstructured_citation>Yin BB, Liew KM. Machine learning and materials informatics approaches for evaluating interfacial properties of fiber-reinforced composites. Compos Struct. 2021;277:114490.</unstructured_citation>
						 <doi>10.1016/j.compstruct.2021.114490</doi> 					</citation>
          					<citation key="rk-10.68159/n403891302-9a2525d4-89c9-4b8f-b668-200294fcf130">
					  <unstructured_citation>Frydrych K, Karimi K, Pecelerowicz M, Alvarez R. Materials informatics for mechanical deformation: A review of applications and challenges. Materials. 2021;14(19):5764.</unstructured_citation>
						 <doi>10.3390/ma14195764</doi> 					</citation>
          					<citation key="rk-10.68159/n403891302-6920f9f7-1644-4ad3-b01f-c263dc0d5a4c">
					  <unstructured_citation>Yao M, Wang Y, Li X, Sheng Y, Huo H, Xi L, et al. Materials informatics platform with three dimensional structures, workflow and thermoelectric applications. Sci Data. 2021;8(1):210.</unstructured_citation>
						 <doi>10.1038/s41597-021-01022-6</doi> 					</citation>
          					<citation key="rk-10.68159/n403891302-6f587d67-e1dc-4608-9061-98320923892a">
					  <unstructured_citation>Hong S, Liow CH, Yuk JM, Byon HR, Yang Y, Cho EA. Reducing time to discovery: Materials and molecular modeling, imaging, informatics, and integration. ACS Nano. 2021;15(3):3627-52.</unstructured_citation>
						 <doi>10.1021/acsnano.1c00211</doi> 					</citation>
          					<citation key="rk-10.68159/n403891302-6b96e0af-6e7e-4bd2-91f5-c437a9bcca46">
					  <unstructured_citation>Haghighatlari M, Vishwakarma G. ChemML: A machine learning and informatics program package for the analysis, mining, and modeling of chemical and materials data. WIREs Comput Mol Sci. 2020;10(6):e1458.</unstructured_citation>
						 <doi>10.1002/wcms.1458</doi> 					</citation>
          					<citation key="rk-10.68159/n403891302-aef5f851-6750-41c3-9284-d79bc0225304">
					  <unstructured_citation>Zhao XG, Zhou K, Xing B, Zhao R, Luo S, Li T, et al. JAMIP: An artificial-intelligence aided data-driven infrastructure for computational materials informatics. Sci Bull. 2021;66(23):2467-70.</unstructured_citation>
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          					<citation key="rk-10.68159/n403891302-a43b592a-6b94-499b-a0d3-7245921ae113">
					  <unstructured_citation>Fuhr AS, Sumpter BG. Deep generative models for materials discovery and machine learning-accelerated innovation. Front Mater. 2022;9:865270.</unstructured_citation>
						 <doi>10.3389/fmats.2022.865270</doi> 					</citation>
          					<citation key="rk-10.68159/n403891302-1df96959-b854-4a1b-af3b-57371ce76aaf">
					  <unstructured_citation>Menon D, Ranganathan R. A generative approach to materials discovery, design, and optimization. ACS Omega. 2022;7(24):20700-15.</unstructured_citation>
						 <doi>10.1021/acsomega.2c03264</doi> 					</citation>
          					<citation key="rk-10.68159/n403891302-9747043e-d01e-4916-baf3-9be159ef434e">
					  <unstructured_citation>Lyngby P, Thygesen KS. Data-driven discovery of 2D materials by deep generative models. npj Comput Mater. 2022;8(1):1-9.</unstructured_citation>
						 <doi>10.1038/s41524-022-00923-3</doi> 					</citation>
          					<citation key="rk-10.68159/n403891302-f9a065cd-c025-4f3c-a9d9-97e2851c5aef">
					  <unstructured_citation>Pilania G. Machine learning in materials science: From explainable predictions to autonomous design. Comput Mater Sci. 2021;197:110585.</unstructured_citation>
						 <doi>10.1016/j.commatsci.2021.110585</doi> 					</citation>
          					<citation key="rk-10.68159/n403891302-22d5a072-5ca5-4709-be25-1204c7b881bf">
					  <unstructured_citation>Hellman A. A brief overview of deep generative models and how they can be used to discover new electrode materials. Curr Opin Electrochem. 2025;45:101456.</unstructured_citation>
						 <doi>10.1016/j.coelec.2024.101456</doi> 					</citation>
          					<citation key="rk-10.68159/n403891302-a714d3ff-af58-4074-ae9c-54204ba65993">
					  <unstructured_citation>Kalinin SV, Dyck O, Jesse S, Ziatdinov M. Exploring order parameters and dynamic processes in disordered systems via variational autoencoders. Sci Adv. 2021;7(15):abd5084.</unstructured_citation>
						 <doi>10.1126/sciadv.abd5084</doi> 					</citation>
          					<citation key="rk-10.68159/n403891302-099910bb-b047-488a-a889-72c58d906f0b">
					  <unstructured_citation>Ji Y, Koeppe A, Altschuh P, Rajagopal D, Zhao Y. Towards automatic feature extraction and sample generation of grain structure by variational autoencoder. J Mater Sci. 2024;59(15):6225.</unstructured_citation>
						 <doi>10.1007/s10853-024-09569-6</doi> 					</citation>
          					<citation key="rk-10.68159/n403891302-f8fd0c59-5d2f-43ad-a40c-dd51050473f6">
					  <unstructured_citation>Biswas A, Ziatdinov M, Kalinin SV. Combining variational autoencoders and physical bias for improved microscopy data analysis. Mach Learn Sci Technol. 2023;4(2):025002.</unstructured_citation>
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          					<citation key="rk-10.68159/n403891302-ca3b53d5-7adc-4e9d-a8c9-2af0a6b7d85a">
					  <unstructured_citation>Attari V, Khatamsaz D, Allaire D, Arroyave R. Towards inverse microstructure-centered materials design using generative phase-field modeling and deep variational autoencoders. Acta Mater. 2023;255:118947.</unstructured_citation>
						 <doi>10.1016/j.actamat.2023.118947</doi> 					</citation>
          					<citation key="rk-10.68159/n403891302-3056d8d9-50aa-4102-a76f-ea293dd166c8">
					  <unstructured_citation>Baima J, Goryaeva AM, Swinburne TD, Maillet JB, Brassart B, Marinica MC, et al. Capabilities and limits of autoencoders for extracting collective variables in atomistic materials science. Phys Chem Chem Phys. 2022;24(24):14517-29.</unstructured_citation>
						 <doi>10.1039/d2cp01917e</doi> 					</citation>
          					<citation key="rk-10.68159/n403891302-2d2973ab-cb99-4b57-9cf1-01fab5518fee">
					  <unstructured_citation>Valleti M, Ziatdinov M, Liu Y, Kalinin SV. Physics and chemistry from parsimonious representations: Image analysis via invariant variational autoencoders. npj Comput Mater. 2024;10(1):125.</unstructured_citation>
						 <doi>10.1038/s41524-024-01250-5</doi> 					</citation>
          					<citation key="rk-10.68159/n403891302-3dce88c5-79e1-47ba-91ee-7b7a12849d45">
					  <unstructured_citation>Kim Y, Park HK, Jung J, Asghari-Rad P, Lee S, Kim JY. Exploration of optimal microstructure and mechanical properties in continuous microstructure space using a variational autoencoder. Mater Des. 2021;204:109697.</unstructured_citation>
						 <doi>10.1016/j.matdes.2021.109697</doi> 					</citation>
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					  <unstructured_citation>Batra R, Dai H, Huan TD, Chen L, Kim C, Guttenberg N, et al. Polymers for extreme conditions designed using syntax-directed variational autoencoders. Chem Mater. 2020;32(24):10458-68.</unstructured_citation>
						 <doi>10.1021/acs.chemmater.0c03332</doi> 					</citation>
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					  <unstructured_citation>Prifti E, Buban JP, Thind AS, Klie RF. Variational convolutional autoencoders for anomaly detection in scanning transmission electron microscopy. Small. 2023;19(26):2205977.</unstructured_citation>
						 <doi>10.1002/smll.202205977</doi> 					</citation>
          					<citation key="rk-10.68159/n403891302-9ca77430-7fcd-42a1-ad2c-421e35320017">
					  <unstructured_citation>Sha W, Guo Y, Yuan Q, Tang S, Zhang X. Artificial intelligence to power the future of materials science and engineering. Adv Intell Syst. 2020;2(6):2000143.</unstructured_citation>
						 <doi>10.1002/aisy.201900143</doi> 					</citation>
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