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			<depositor_name>Institute for Advanced Materials Research Press</depositor_name>
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		<registrant>Institute for Advanced Materials Research Press</registrant>
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				<full_title>Journal of Computational and Data-Driven Materials Engineering</full_title>
				<abbrev_title>J. Comput. Data-Driven Mater. Eng.</abbrev_title>
				<issn>3149-9368</issn>
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				<publication_date>
					<year>2023</year>
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					<volume>2</volume>
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				<issue>2</issue>
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					<title>Perspective: Embedding Domain Knowledge Is Not Optional — A Position on Physics-Constrained Materials GNNs</title>
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								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>Carlos</given_name>
            <surname>Ramirez</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Elena</given_name>
            <surname>Torres</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Pablo</given_name>
            <surname>Ortega</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Sofia</given_name>
            <surname>Mendes</surname>
					</person_name>
          				</contributors>
								<publication_date>
					<year>2023</year>
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          					<citation key="rk-10.68159/g473686991-b14ab90d-9b8c-4c36-9886-7d37cbb6bf01">
					  <unstructured_citation>Bartók AP, De S, Poelking C, Bernstein N, Kermode JR, Csányi G, et al. Machine learning unifies the modeling of materials and molecules. Sci Adv. 2017;3(12).</unstructured_citation>
						 <doi>10.1126/sciadv.1701816</doi> 					</citation>
          					<citation key="rk-10.68159/g473686991-abfe06ef-ab5a-40b8-b7b8-53ba6c57cae5">
					  <unstructured_citation>Schütt KT, Sauceda HE, Kindermans PJ, Tkatchenko A, Müller KR. SchNet: A deep learning architecture for molecules and materials. J Chem Phys. 2018;148(24):241722.</unstructured_citation>
						 <doi>10.1063/1.5019779</doi> 					</citation>
          					<citation key="rk-10.68159/g473686991-54f38391-9cd5-48f8-89be-b36ea912fde7">
					  <unstructured_citation>Xie T, Grossman JC. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys Rev Lett. 2018;120(14):145301.</unstructured_citation>
						 <doi>10.1103/PhysRevLett.120.145301</doi> 					</citation>
          					<citation key="rk-10.68159/g473686991-4576ea3a-0464-4933-91df-d408f048ad71">
					  <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>
						 <doi>10.1021/acs.chemmater.9b01294</doi> 					</citation>
          					<citation key="rk-10.68159/g473686991-e39c6fb2-c628-459b-b4b8-d9aedce8c302">
					  <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>
						 <doi>10.1038/s43246-022-00315-6</doi> 					</citation>
          					<citation key="rk-10.68159/g473686991-2d4f721b-1c4c-4c55-9ca6-ebbdb65f5afd">
					  <unstructured_citation>Karniadakis GE, Kevrekidis IG, Lu L, Perdikaris P, Wang S, Yang L. Physics-informed machine learning. Nat Rev Phys. 2021;3(6):422-40.</unstructured_citation>
						 <doi>10.1038/s42254-021-00314-5</doi> 					</citation>
          					<citation key="rk-10.68159/g473686991-f57a9509-0c56-4381-a5f6-a21725cf37e6">
					  <unstructured_citation>Morgan JP, Paiement A, Klinke C. Domain-informed graph neural networks: A quantum chemistry case study. Neural Netw. 2023;165:938-52.</unstructured_citation>
						 <doi>10.1016/j.neunet.2023.06.030</doi> 					</citation>
          					<citation key="rk-10.68159/g473686991-6681b932-4c94-47b9-a01e-9c70ef45a613">
					  <unstructured_citation>Wang R, Zou Y, Zhang C, Wang X, Yang M, Xu D. Combining crystal graphs and domain knowledge in machine learning to predict metal-organic frameworks performance in methane adsorption. Microporous Mesoporous Mater. 2022;331:111666.</unstructured_citation>
						 <doi>10.1016/j.micromeso.2021.111666</doi> 					</citation>
          					<citation key="rk-10.68159/g473686991-af8463c4-897e-44d3-ad41-ccd6f58a9da6">
					  <unstructured_citation>Bødker ML, Bauchy M, Du T, Mauro JC, Smedskjaer MM. Predicting glass structure by physics-informed machine learning. NPJ Comput Mater. 2022;8(1):192.</unstructured_citation>
						 <doi>10.1038/s41524-022-00882-9</doi> 					</citation>
          					<citation key="rk-10.68159/g473686991-6dd00e6a-57b1-4511-8ca8-663cd2487c4e">
					  <unstructured_citation>Khatamsaz D, Neuberger R, Roy AM, Zadeh SH, Otis R, Arróyave R. A physics informed bayesian optimization approach for material design: Application to NiTi shape memory alloys. NPJ Comput Mater. 2023;9(1):221.</unstructured_citation>
						 <doi>10.1038/s41524-023-01173-7</doi> 					</citation>
          					<citation key="rk-10.68159/g473686991-0c37a032-c4e3-4bd2-9a6f-db2916cb0488">
					  <unstructured_citation>Wu JL, Xiao H, Paterson E. Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework. Phys Rev Fluids. 2018;3(7):074602.</unstructured_citation>
						 <doi>10.1103/PhysRevFluids.3.074602</doi> 					</citation>
          					<citation key="rk-10.68159/g473686991-c3f83c0f-7de9-4725-8bcc-fb02a7ccee80">
					  <unstructured_citation>Choudhary K, DeCost B. Atomistic line graph neural network for improved materials property predictions. NPJ Comput Mater. 2021;7(1):185.</unstructured_citation>
						 <doi>10.1038/s41524-021-00650-1</doi> 					</citation>
          					<citation key="rk-10.68159/g473686991-c5821471-a8dd-4fef-9416-9a1e5a779377">
					  <unstructured_citation>Jørgensen PB, Garijo del Río E, Schmidt MN, Jacobsen KW. Materials property prediction using symmetry-labeled graphs as atomic-position independent descriptors. Phys Rev B. 2019;100(10):104114.</unstructured_citation>
						 <doi>10.1103/PhysRevB.100.104114</doi> 					</citation>
          					<citation key="rk-10.68159/g473686991-c190398e-16ac-439b-8dc7-b21fdd853165">
					  <unstructured_citation>Cheng J, Zhang C, Dong L. A geometric-information-enhanced crystal graph network for predicting properties of materials. Commun Mater. 2021;2(1):92.</unstructured_citation>
						 <doi>10.1038/s43246-021-00194-3</doi> 					</citation>
          					<citation key="rk-10.68159/g473686991-7c62a3f9-696d-4d83-80ac-933fbdb57881">
					  <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).</unstructured_citation>
						 <doi>10.1126/sciadv.adi3245</doi> 					</citation>
          					<citation key="rk-10.68159/g473686991-d4335448-69dc-4e10-81ee-9a6beaf501e9">
					  <unstructured_citation>Kaba SO, Ravanbakhsh S. Equivariant networks for crystal structures. Adv Neural Inf Process Syst. 2022;35:4150-64.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g473686991-c81ee014-f60e-4f8a-bd97-d7fa01a98ec6">
					  <unstructured_citation>Pakornchote T, Ektarawong A, Chotibut T. StrainTensorNet: Predicting crystal structure elastic properties using SE(3)-equivariant graph neural networks. Phys Rev Res. 2023;5(4):043198.</unstructured_citation>
						 <doi>10.1103/PhysRevResearch.5.043198</doi> 					</citation>
          					<citation key="rk-10.68159/g473686991-0e6d7a24-714b-4134-a5db-6d966a6cb158">
					  <unstructured_citation>Landes FP, Furtlehner C. Equivariant graph neural networks for amorphous materials [Internet]. Gif-sur-Yvette: LISN, Université Paris-Saclay; 2021 [cited 2026 Jun 21]. Available from: https://www.lri.fr/~gcharpia/LISN_INRIA_Equivariant_GNN_for_amorphous_materials.pdf</unstructured_citation>
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          					<citation key="rk-10.68159/g473686991-c37b958e-95de-43dd-80a6-a8ec1a427510">
					  <unstructured_citation>Satorras VG, Hoogeboom E, Welling M. E(n) equivariant graph neural networks. In: Proceedings of the 38th International conference on machine learning. Proc Mach Learn Res. 2021;139:9323-32.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g473686991-75972dec-4ce6-4c62-80d8-819da4b918e1">
					  <unstructured_citation>Sudmanns M, Bach J, Weygand D, Schulz K. Data-driven exploration and continuum modeling of dislocation networks. Model Simul Mater Sci Eng. 2020;28(6):065001.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g473686991-2b9449be-3d15-40a3-b74c-3e3ad6fce04a">
					  <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(1):2453.</unstructured_citation>
						 <doi>10.1038/s41467-022-29939-5</doi> 					</citation>
          					<citation key="rk-10.68159/g473686991-8cde1cae-ecf4-4f3c-8b12-fb48adc1b514">
					  <unstructured_citation>Batatia I, Kovács DP, Simm G, Ortner C, Csányi G. MACE: Higher order equivariant message passing neural networks for fast and accurate force fields. Adv Neural Inf Process Syst. 2022;35:11423-36.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g473686991-829ba51b-f8e3-4ea4-9f18-972899ec5f23">
					  <unstructured_citation>Huo H, Rupp M. Unified representation of molecules and crystals for machine learning. Mach Learn Sci Technol. 2022;3(4):045017.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g473686991-0d39cca7-1a84-4f34-8e84-b2cd62c26c24">
					  <unstructured_citation>Atz K, Grisoni F, Schneider G. Geometric deep learning on molecular representations. Nat Mach Intell. 2021;3(12):1023-32.</unstructured_citation>
						 <doi>10.1038/s42256-021-00418-8</doi> 					</citation>
          					<citation key="rk-10.68159/g473686991-1c72b796-1dc0-433c-ac50-e8fa4c16edea">
					  <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>
						 <doi>10.1038/s41524-022-00884-7</doi> 					</citation>
          					<citation key="rk-10.68159/g473686991-19e08e24-72f7-415f-9de5-209bcdb242d0">
					  <unstructured_citation>Oliva M, Banik S, Josifovski J, Knoll A. Graph neural networks for relational inductive bias in vision-based deep reinforcement learning of robot control. In: 2022 International Joint Conference on Neural Networks (IJCNN); 2022. p. 1-9.</unstructured_citation>
						 <doi>10.1109/IJCNN55064.2022.9892101</doi> 					</citation>
          					<citation key="rk-10.68159/g473686991-e432eff9-e0ef-4939-beb7-2081bb4d19a1">
					  <unstructured_citation>Bishnoi S, Bhattoo R, Ranu S, Krishnan NMA. Enhancing the inductive biases of graph neural ODE for modeling dynamical systems [Preprint]. arXiv; 2022. arXiv:2209.10740.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g473686991-bd1142cd-4a3f-4f4c-b4c6-8716e57f7114">
					  <unstructured_citation>Ringsquandl M, Sellami H, Hildebrandt M, Beyer D, Henselmeyer S, Weber S, et al. Power to the relational inductive bias: Graph neural networks in electrical power grids. In: Proceedings of the 30th ACM International conference on information &amp; knowledge management. New York: Association for Computing Machinery; 2021. p. 1538-47.</unstructured_citation>
						 <doi>10.1145/3459637.3482464</doi> 					</citation>
          					<citation key="rk-10.68159/g473686991-897fa78a-86db-442b-80ae-cf5101573e40">
					  <unstructured_citation>Angione C, Silverman E, Yaneske E. Using machine learning as a surrogate model for agent-based simulations. PLoS One. 2022;17(2).</unstructured_citation>
						 <doi>10.1371/journal.pone.0263150</doi> 					</citation>
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