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			<depositor_name>Institute for Advanced Materials Research Press</depositor_name>
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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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					<year>2023</year>
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					<volume>2</volume>
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				<issue>2</issue>
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					<title>Information-Theoretic Limits of Transfer Learning Across Chemical Spaces in Materials GNNs</title>
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          					<person_name sequence="first" contributor_role="author">
            <given_name>Maria</given_name>
            <surname>Silva</surname>
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            <given_name>Joao</given_name>
            <surname>Pereira</surname>
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          					<citation key="rk-10.68159/j743528385-316b0f9e-f599-4f06-a922-1922695ec051">
					  <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/j743528385-17caaa32-e70b-4279-b65d-8de5db8868c9">
					  <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/j743528385-d61cfe80-b5a1-4e40-b0c6-17780c678f91">
					  <unstructured_citation>Blanchard G, Deshmukh AA, Dogan U, Lee G, Scott C. Domain generalization by marginal transfer learning. J Mach Learn Res. 2021;22(2):1-55.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j743528385-be4f5ab9-9b07-48f0-9214-b5d7520ec6b2">
					  <unstructured_citation>Chen S, Sahinidis NV, Gao C. Transfer learning in information criteria-based feature selection. J Mach Learn Res. 2022;23(134):1-105.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j743528385-2fb48fe4-156b-45cf-bb42-d3f636e7feb0">
					  <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/j743528385-cee8ab77-0f4f-4b18-b9fb-92b59c6bf596">
					  <unstructured_citation>Xu K, Hu W, Leskovec J, Jegelka S. How powerful are graph neural networks? arXiv:1810.00826 [Preprint]. 2018.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j743528385-027a6e6a-2355-48f1-a835-9ed1c3c5df0f">
					  <unstructured_citation>Shen X, Pan S, Choi KS, Zhou X. Domain-adaptive message passing graph neural network. Neural Netw. 2023;164:439-54.</unstructured_citation>
						 <doi>10.1016/j.neunet.2023.04.038</doi> 					</citation>
          					<citation key="rk-10.68159/j743528385-e8f2a125-b7d2-407a-91a2-c7aa34d5aa16">
					  <unstructured_citation>Gerace F, Saglietti L, Sarao Mannelli S, Saxe A, Zdeborová L. Probing transfer learning with a model of synthetic correlated datasets. Mach Learn Sci Technol. 2022;3(1):015030.</unstructured_citation>
						 <doi>10.1088/2632-2153/ac4f3f</doi> 					</citation>
          					<citation key="rk-10.68159/j743528385-dc1b2054-7e4c-4528-a4b6-febf2c68be17">
					  <unstructured_citation>Mallillin LLD. Different domains in learning and the academic performance of the students. Journal of Educational System. 2020;4(1):1-11.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j743528385-d72a35b7-9bfe-42b0-a49c-1fbf3de71557">
					  <unstructured_citation>Fang Z, Lu J, Liu A, Liu F, Zhang G. Learning bounds for open-set learning. In: Proceedings of the 38th International Conference on Machine Learning; 2021 Jul 18-24; Virtual. PMLR; 2021. p. 3122-32.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j743528385-9eb3d798-462b-4df2-9dcc-75f526ed4373">
					  <unstructured_citation>Li X, Zhang W. Deep learning-based partial domain adaptation method on intelligent machinery fault diagnostics. IEEE Trans Ind Electron. 2021;68(5):4351-61.</unstructured_citation>
						 <doi>10.1109/TIE.2020.2984968</doi> 					</citation>
          					<citation key="rk-10.68159/j743528385-d733a5ff-49e1-44aa-89c8-8665d5ff5e70">
					  <unstructured_citation>Choudhary K, DeCost B, Major L, Butler K, Thiyagalingam J, Tavazza F. Unified graph neural network force-field for the periodic table: Solid state applications. Digit Discov. 2023;2(2):346-55.</unstructured_citation>
						 <doi>10.1039/D2DD00096B</doi> 					</citation>
          					<citation key="rk-10.68159/j743528385-a5353864-0d09-4749-a93a-dc20e83b7dd8">
					  <unstructured_citation>Chen G, Song Z, Qi Z, Sundmacher K. Generalizing property prediction of ionic liquids from limited labeled data: A one-stop framework empowered by transfer learning. Digit Discov. 2023;2(3):591-601.</unstructured_citation>
						 <doi>10.1039/D3DD00040K</doi> 					</citation>
          					<citation key="rk-10.68159/j743528385-dbc6fc22-5579-4fd7-9ab9-37a3f963c321">
					  <unstructured_citation>Hoffmann N, Schmidt J, Botti S, Marques MAL. Transfer learning on large datasets for the accurate prediction of material properties. Digit Discov. 2023;2(5):1368-79.</unstructured_citation>
						 <doi>10.1039/D3DD00030C</doi> 					</citation>
          					<citation key="rk-10.68159/j743528385-cf36e284-c7e1-4307-893a-9411022b6503">
					  <unstructured_citation>Zhu W, Luo J, White AD. Federated learning of molecular properties with graph neural networks in a heterogeneous setting. Patterns (N Y). 2022;3(6):100521.</unstructured_citation>
						 <doi>10.1016/j.patter.2022.100521</doi> 					</citation>
          					<citation key="rk-10.68159/j743528385-822d035d-4074-4230-8aa0-70a586923009">
					  <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/j743528385-4379c0bd-146f-42b6-9d56-b10dc4e23549">
					  <unstructured_citation>Weinberger N. Generalization bounds and algorithms for learning to communicate over additive noise channels. IEEE Trans Inf Theory. 2022;68(3):1886-921.</unstructured_citation>
						 <doi>10.1109/TIT.2022.3176371</doi> 					</citation>
          					<citation key="rk-10.68159/j743528385-a82da74c-1e40-417d-ba94-f9b8099ca3bd">
					  <unstructured_citation>Di X, Yu P, Bu R, Sun M. Mutual information maximization in graph neural networks. In: 2020 International Joint Conference on Neural Networks (IJCNN); 2020 Jul 19-24; Glasgow, UK. IEEE; 2020. p. 1-7.</unstructured_citation>
						 <doi>10.1109/IJCNN48605.2020.9207076</doi> 					</citation>
          					<citation key="rk-10.68159/j743528385-97d7c646-42f7-4c1b-9443-34f7af2d7ddc">
					  <unstructured_citation>Peng Z, Huang W, Luo M, Zheng Q, Rong Y, Xu T, et al. Graph representation learning via graphical mutual information maximization. In: Proceedings of The Web Conference 2020; 2020 Apr 20-24; Taipei, Taiwan. New York: ACM; 2020. p. 259-70.</unstructured_citation>
						 <doi>10.1145/3366423.3380112</doi> 					</citation>
          					<citation key="rk-10.68159/j743528385-3cedc727-c0fa-4f06-8ffb-623a3ceb3999">
					  <unstructured_citation>Pandeva T, Bakker T, Naesseth CA, Forré P. E-valuating classifier two-sample tests [Preprint]. arXiv; 2022. arXiv:2210.13027.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j743528385-a13ff2ee-9741-4cbc-9f1f-b44610bae31f">
					  <unstructured_citation>Wu X, Manton JH, Aickelin U, Zhu J. Information-theoretic analysis for transfer learning. In: 2020 IEEE International Symposium on Information Theory (ISIT); 2020 Jun 21-26. IEEE; 2020. p. 2819-24.</unstructured_citation>
						 <doi>10.1109/ISIT44484.2020.9173989</doi> 					</citation>
          					<citation key="rk-10.68159/j743528385-cf75a517-61c2-4ad4-8441-1729a9106a6d">
					  <unstructured_citation>Jose ST, Simeone O. Information-theoretic bounds on transfer generalization gap based on Jensen-Shannon divergence. In: 2021 29th European Signal Processing Conference (EUSIPCO); 2021 Aug 23-27; Dublin, Ireland. IEEE; 2021. p. 1461-5.</unstructured_citation>
						 <doi>10.23919/EUSIPCO54536.2021.9616270</doi> 					</citation>
          					<citation key="rk-10.68159/j743528385-eee2901b-7091-4b87-9f70-7998e0a2f440">
					  <unstructured_citation>Esposito AR, Gastpar M, Issa I. Generalization error bounds via Rényi-, f-divergences and maximal leakage. IEEE Trans Inf Theory. 2021;67(8):4986-5004.</unstructured_citation>
						 <doi>10.1109/TIT.2021.3085190</doi> 					</citation>
          					<citation key="rk-10.68159/j743528385-c88bfbb3-3cb9-456c-9060-8d40ec3e1cc0">
					  <unstructured_citation>Zhou R, Tian C, Liu T. Individually conditional individual mutual information bound on generalization error. IEEE Trans Inf Theory. 2022;68(5):3304-16.</unstructured_citation>
						 <doi>10.1109/TIT.2022.3144615</doi> 					</citation>
          					<citation key="rk-10.68159/j743528385-a8f1dd5b-0be4-4ec8-a7ef-5f6ed2c64596">
					  <unstructured_citation>Chen Q, Marchand M. Algorithm-dependent bounds for representation learning of multi-source domain adaptation. In: Proceedings of the 26th International Conference on Artificial Intelligence and Statistics; 2023 Apr 25-27; Valencia, Spain. PMLR; 2023. p. 10368-94.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j743528385-d3ed20f3-d873-455a-9214-def10148b610">
					  <unstructured_citation>Rojas-Carulla M, Schölkopf B, Turner R, Peters J. Invariant models for causal transfer learning. J Mach Learn Res. 2018;19(36):1-34.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j743528385-ff752f10-fd4f-4818-8ad8-649cb2835b40">
					  <unstructured_citation>Park GY, Lee SW. Information-theoretic regularization for multi-source domain adaptation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV); 2021 Oct; Virtual. IEEE; 2021. p. 9214-23.</unstructured_citation>
						 <doi>10.1109/ICCV48922.2021.00908</doi> 					</citation>
          					<citation key="rk-10.68159/j743528385-2c272088-e4a2-4fed-9728-caf1bbfc3c73">
					  <unstructured_citation>Lv S. Generalization bounds for graph convolutional neural networks via Rademacher complexity [Preprint]. arXiv; 2021. arXiv:2102.10234</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j743528385-a9597368-2882-4907-8b2a-31b8569d26ac">
					  <unstructured_citation>Ahn SS, Hu S, Dai Z, Damianou A, Lawrence N. Mutual information guided distillation for transfer learning. In: 32nd Conference on Neural Information Processing Systems; 2018 Dec 3-8; Montréal, Canada.</unstructured_citation>
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