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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>2024</year>
				</publication_date>
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					<volume>3</volume>
				</journal_volume>
				<issue>2</issue>
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			<journal_article publication_type="full_text">
				<titles>
					<title>Generalization in Materials AI: A Theoretical Distinction between New Compositions, New Structures, and New Physics</title>
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								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>Hiroshi</given_name>
            <surname>Nakamura</surname>
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          					<person_name sequence="additional" contributor_role="author">
            <given_name>Yuta</given_name>
            <surname>Kato</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Aiko</given_name>
            <surname>Morita</surname>
					</person_name>
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								<publication_date>
					<year>2024</year>
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					  <unstructured_citation>Morgan D, Jacobs R. Opportunities and challenges for machine learning in materials science. Annu Rev Mater Res. 2020;50:71-103.</unstructured_citation>
						 <doi>10.1146/annurev-matsci-070218-010015</doi> 					</citation>
          					<citation key="rk-10.68159/y867030804-c1e43f3b-cc6a-44be-9ada-e3ddcd0df76a">
					  <unstructured_citation>Dunn A, Wang Q, Ganose AM, Dopp D, Jain A. Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm. npj Comput Mater. 2020;6:1-10.</unstructured_citation>
						 <doi>10.1038/s41524-020-00406-3</doi> 					</citation>
          					<citation key="rk-10.68159/y867030804-e48aa152-ea84-442c-a2f3-bfff3aeb6d79">
					  <unstructured_citation>Goodall REA, Lee AA. Predicting materials properties without crystal structure: deep representation learning from stoichiometry. Nat Commun. 2020;11:6280.</unstructured_citation>
						 <doi>10.1038/s41467-020-19964-7</doi> 					</citation>
          					<citation key="rk-10.68159/y867030804-54b61685-1865-45b6-a9d3-79445b7b6305">
					  <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;3:93.</unstructured_citation>
						 <doi>10.1038/s43246-022-00315-6</doi> 					</citation>
          					<citation key="rk-10.68159/y867030804-edebd875-157d-408d-b4ea-80d2e1a34924">
					  <unstructured_citation>Merchant A, Batzner S, Schoenholz SS, Aykol M, Cheon G, Cubuk ED. Scaling deep learning for materials discovery. Nature. 2023;624:80-5.</unstructured_citation>
						 <doi>10.1038/s41586-023-06735-9</doi> 					</citation>
          					<citation key="rk-10.68159/y867030804-a3d35406-0f7e-4df0-96cc-733786880926">
					  <unstructured_citation>Batzner S, Musaelian A, Sun L, Geiger M, Mailoa JP, Kornbluth M, et al. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. Nat Commun. 2022;13:2453.</unstructured_citation>
						 <doi>10.1038/s41467-022-29939-5</doi> 					</citation>
          					<citation key="rk-10.68159/y867030804-9e24bc43-750e-4f30-9678-181774a62e41">
					  <unstructured_citation>Chen C, Zuo Y, Ye W, Li X, Ong SP. Learning properties of ordered and disordered materials from multi-fidelity data. Nat Comput Sci. 2021;1:46-53.</unstructured_citation>
						 <doi>10.1038/s43588-020-00002-x</doi> 					</citation>
          					<citation key="rk-10.68159/y867030804-082ba2f0-0671-43d4-bbf4-6ee7bc94a97c">
					  <unstructured_citation>Karamad M, Sinha S, Shi Y, Siahrostami S, Gates ID, Farimani A, Orbital graph convolutional neural network for material property prediction. Phys Rev Mater. 2020;4:093801.</unstructured_citation>
						 <doi>10.1103/PhysRevMaterials.4.093801</doi> 					</citation>
          					<citation key="rk-10.68159/y867030804-add35c6c-e329-4315-a484-401cda8266f3">
					  <unstructured_citation>Noh J, Gu GH, Kim S, Jung Y. Machine-enabled inverse design of inorganic solid materials: promises and challenges. Chem Sci. 2020;11:4871-81.</unstructured_citation>
						 <doi>10.1039/D0SC00594K</doi> 					</citation>
          					<citation key="rk-10.68159/y867030804-8033d80d-5fac-4a15-86cc-fbf083893818">
					  <unstructured_citation>Friederich P, Häse F, Proppe J, Aspuru-Guzik A. Machine-learned potentials for next-generation matter simulations. Nat Mater. 2021;20:750-61.</unstructured_citation>
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          					<citation key="rk-10.68159/y867030804-8e2d689f-547d-4504-bf3c-1fbe6badca10">
					  <unstructured_citation>Schmidt J, Pettersson L, Verdozzi C, Botti S, Marques MAL. Crystal graph attention networks for the prediction of stable materials. Sci Adv. 2021;7:eabi7948.</unstructured_citation>
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          					<citation key="rk-10.68159/y867030804-796e4681-dd2b-424c-a490-57b7eccd8064">
					  <unstructured_citation>von Lilienfeld OA, Burke K. Retrospective on a decade of machine learning for chemical discovery. Nat Commun. 2020;11:1-4.</unstructured_citation>
						 <doi>10.1038/s41467-020-18556-9</doi> 					</citation>
          					<citation key="rk-10.68159/y867030804-0975ad99-046b-4c0e-b976-ca59fb15aab6">
					  <unstructured_citation>Bartel CJ, Trewartha A, Wang Q, Dunn A, Jain A, Ceder G. A critical examination of compound stability predictions from machine-learned formation energies. npj Comput Mater. 2020;6:97.</unstructured_citation>
						 <doi>10.1038/s41524-020-00393-5</doi> 					</citation>
          					<citation key="rk-10.68159/y867030804-a8d3ab9b-aeea-44a9-aad5-7afd602d55cc">
					  <unstructured_citation>Wang H-C, Botti S, Marques MAL. Predicting stable crystalline compounds using chemical similarity. npj Comput Mater. 2021;7:12.</unstructured_citation>
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          					<citation key="rk-10.68159/y867030804-cab67c8f-5a26-4925-bbff-cc98a7b69465">
					  <unstructured_citation>Furness JW, Kaplan AD, Ning J, Perdew JP, Sun J. Accurate and numerically efficient r²SCAN meta-generalized gradient approximation. J Phys Chem Lett. 2020;11:8208-15.</unstructured_citation>
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					  <unstructured_citation>Deringer VL, Caro MA, Csányi G. A general-purpose machine-learning force field for bulk and nanostructured phosphorus. Nat Commun. 2020;11:5461.</unstructured_citation>
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					  <unstructured_citation>Yao Z, Sánchez-Lengeling B, Jayaraman JD. Inverse design of nanoporous crystalline reticular materials with deep generative models. Nat Mach Intell. 2021;3:76-86.</unstructured_citation>
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          					<citation key="rk-10.68159/y867030804-66dd994d-73a8-45b5-a167-3ce17780e026">
					  <unstructured_citation>Atz K, Grisoni F, Schneider G. Geometric deep learning on molecular representations. Nat Mach Intell. 2021;3:1023-32.</unstructured_citation>
						 <doi>10.1038/s42256-021-00418-8</doi> 					</citation>
          					<citation key="rk-10.68159/y867030804-517c83bd-7795-4fc7-b549-9a7d0a85833c">
					  <unstructured_citation>Zhou J, Cui G, Hu S, Zhang Z, Yang C, Liu Z, et al. Graph neural networks: a review of methods and applications. AI Open. 2021;2:57-81.</unstructured_citation>
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          					<citation key="rk-10.68159/y867030804-56993ad5-49d8-4f4c-85cc-9f9c6edf47a0">
					  <unstructured_citation>Wu Z, Pan S, Chen F, Long G, Zhang C, Yu PS, et al. A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Syst. 2021;32:4-24.</unstructured_citation>
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          					<citation key="rk-10.68159/y867030804-0933ce32-de08-4185-9d50-7900ca718eb1">
					  <unstructured_citation>Cheng J, Zhang C, Dong L. A geometric-information-enhanced crystal graph network for predicting properties of materials. Commun Mater. 2021;2:92.</unstructured_citation>
						 <doi>10.1038/s43246-021-00194-3</doi> 					</citation>
          					<citation key="rk-10.68159/y867030804-4bfd4d52-f1cf-4cd3-8b0f-e9d4b9d4f459">
					  <unstructured_citation>Long T, Fortunato NM, Opahle I, Zhang Y, Samathrakis I, Shen C, et al. Constrained crystals deep convolutional generative adversarial network for the inverse design of crystal structures. npj Comput Mater. 2021;7:66.</unstructured_citation>
						 <doi>10.1038/s41524-021-00526-4</doi> 					</citation>
          					<citation key="rk-10.68159/y867030804-0353e609-4b2a-4773-a730-3a7119bf159f">
					  <unstructured_citation>Wang Q, Zhang L. Inverse design of glass structure with deep graph neural networks. Nat Commun. 2021;12:4899.</unstructured_citation>
						 <doi>10.1038/s41467-021-25490-x</doi> 					</citation>
          					<citation key="rk-10.68159/y867030804-5fe787de-d962-44a1-8652-3f43afd78057">
					  <unstructured_citation>Hsu T, Pham TA, Keilbart N, Weitzner S, Chapman J, Xiao P, et al. Efficient and interpretable graph network representation for angle-dependent properties applied to optical spectroscopy. npj Comput Mater. 2022;8:151.</unstructured_citation>
						 <doi>10.1038/s41524-022-00830-9</doi> 					</citation>
          					<citation key="rk-10.68159/y867030804-4236bcdb-62b8-4578-9fe6-12422359ee7e">
					  <unstructured_citation>Chen C, Ong SP. A universal graph deep learning interatomic potential for the periodic table. Nat Comput Sci. 2022;2:718-28.</unstructured_citation>
						 <doi>10.1038/s43588-022-00349-3</doi> 					</citation>
          					<citation key="rk-10.68159/y867030804-8a829690-16e4-4b0e-a220-4f60fdaeba4d">
					  <unstructured_citation>Zhong X, Gallagher B, Liu S, Kailkhura B, Hiszpanski A, Han TJJ, et al. Explainable machine learning in materials science. npj Comput Mater. 2022;8:204.</unstructured_citation>
						 <doi>10.1038/s41524-022-00884-7</doi> 					</citation>
          					<citation key="rk-10.68159/y867030804-d37c812e-3acc-487b-b19b-c0bfc135eb14">
					  <unstructured_citation>Park J, Shim Y, Lee F, Rammohan A, Goyal S, Shim M, et al. Prediction and Interpretation of Polymer Properties Using the Graph Convolutional Network. ACS Polym Au. 2022;2(4):213-22.</unstructured_citation>
						 <doi>10.1021/acspolymersau.1c00050</doi> 					</citation>
          					<citation key="rk-10.68159/y867030804-eb55b6d5-9f95-4026-9c9b-b2b57ce9e2a5">
					  <unstructured_citation>Chen Y, Tang X, Qi X, Li C-G, Xiao R. Learning graph normalization for graph neural networks. Neurocomputing. 2022;493:613-25.</unstructured_citation>
						 <doi>10.1016/j.neucom.2022.01.003</doi> 					</citation>
          					<citation key="rk-10.68159/y867030804-243ea5fb-eac1-4797-ab7b-de84b0032a47">
					  <unstructured_citation>Gastegger M, Schütt KT, Müller K-R. Machine learning of solvent effects on molecular spectra and reactions. Chem Sci. 2021;12:11473-83.</unstructured_citation>
						 <doi>10.1039/D1SC02742E</doi> 					</citation>
          					<citation key="rk-10.68159/y867030804-66e189fb-b360-4f47-b59f-f5fbbadfe84b">
					  <unstructured_citation>Gong S, Yan K, Xie T, Shao-Horn Y, Gomez-Bombarelli R, Ji S, et al. Examining graph neural networks for crystal structures: Limitations and opportunities for capturing periodicity. Sci Adv. 2023;9(45):eadi3245.</unstructured_citation>
						 <doi>10.1126/sciadv.adi3245</doi> 					</citation>
          					<citation key="rk-10.68159/y867030804-89346e95-8c74-4e9a-95fa-80352965a47f">
					  <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;31:3564-72.</unstructured_citation>
						 <doi>10.1021/acs.chemmater.9b01294</doi> 					</citation>
          					<citation key="rk-10.68159/y867030804-6f6e70f1-9b14-4521-bab9-81e997448fe5">
					  <unstructured_citation>von Lilienfeld OA, Müller K-R, Tkatchenko A. Exploring chemical compound space with quantum-based machine learning. Nat Rev Chem. 2020;4:347-58.</unstructured_citation>
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          					<citation key="rk-10.68159/y867030804-363bee60-fff9-4cff-8e8c-17f00e407dc2">
					  <unstructured_citation>Huang B, von Lilienfeld OA. Quantum machine learning using atom-in-molecule-based fragments selected on the fly. Nat Chem. 2020;12:945-51.</unstructured_citation>
						 <doi>10.1038/s41557-020-0527-z</doi> 					</citation>
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					  <unstructured_citation>Christensen AS, Bratholm LA, Faber FA, von Lilienfeld OA. FCHL revisited: faster and more accurate quantum machine learning. J Chem Phys. 2020;152:044107.</unstructured_citation>
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					  <unstructured_citation>Kalinin SV, Ziatdinov M, Vasudevan S, Xie S. Machine learning in scanning transmission electron microscopy. Nat Rev Methods Primers. 2022;2:1-28.</unstructured_citation>
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