<?xml version="1.0" encoding="UTF-8"?><doi_batch version="4.3.7" xmlns="http://www.crossref.org/schema/4.3.7" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.crossref.org/schema/4.3.7 http://www.crossref.org/schema/deposit/crossref4.3.7.xsd">
		<head>
		<doi_batch_id>iamrp.net-ngfm-1791491020-u729969158</doi_batch_id>
		<timestamp>1791491020</timestamp>
		<depositor>
			<depositor_name>Institute for Advanced Materials Research Press</depositor_name>
			<email_address>info@iamrp.net</email_address>
		</depositor>
		<registrant>Institute for Advanced Materials Research Press</registrant>
	</head>
	<body>
		<journal>
			<journal_metadata>
				<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>
			</journal_metadata>
			<journal_issue>
				<publication_date>
					<year>2025</year>
				</publication_date>
				<journal_volume>
					<volume>4</volume>
				</journal_volume>
				<issue>1</issue>
			</journal_issue>
			<journal_article publication_type="full_text">
				<titles>
					<title>Theoretical Foundations of Geometric Deep Learning for Amorphous Structures: Beyond the Periodicity Assumption</title>
				</titles>
								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>Thomas</given_name>
            <surname>Andersen</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Lars</given_name>
            <surname>Nielsen</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Mette</given_name>
            <surname>Sørensen</surname>
					</person_name>
          				</contributors>
								<publication_date>
					<year>2025</year>
				</publication_date>
				<doi_data>
					<doi>10.68159/u729969158</doi>
					<resource>https://iamrp.net/pub/journal/2/article/u729969158</resource>
				</doi_data>
				<citation_list>
          					<citation key="rk-10.68159/u729969158-839fd6c7-2563-4eab-af61-72ca07604f6e">
					  <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/u729969158-21802fca-85a1-4185-a965-27cdfce8d128">
					  <unstructured_citation>Wu X, Wang H, Gong Y, Fan D, Ding P, Li Q, et al. Graph neural networks for molecular and materials representation. J Mater Inf. 2023;3(2):12.</unstructured_citation>
						 <doi>10.20517/jmi.2023.10</doi> 					</citation>
          					<citation key="rk-10.68159/u729969158-3e6eef0f-496e-4627-9d81-8e83662ef175">
					  <unstructured_citation>Duval A, Mathis SV, Joshi CK, Schmidt V, Miret S, Malliaros FD, et al. A hitchhiker&#039;s guide to geometric GNNs for 3D atomic systems. arXiv Preprint. 2023. arXiv:2312.07511.</unstructured_citation>
						 <doi>10.48550/arXiv.2312.07511</doi> 					</citation>
          					<citation key="rk-10.68159/u729969158-bcfd4a59-d593-4955-a548-b2845bcc22ac">
					  <unstructured_citation>Ojih J, Al-Fahdi M, Yao Y, Hu J, Hu M. Graph theory and graph neural network assisted high-throughput crystal structure prediction and screening for energy conversion and storage. J Mater Chem A. 2024;12(14):8502-15.</unstructured_citation>
						 <doi>10.1039/D3TA06190F</doi> 					</citation>
          					<citation key="rk-10.68159/u729969158-fd5fe716-2347-49a7-920e-246846a9632d">
					  <unstructured_citation>Deng B, Zhong P, Jun KJ, Riebesell J, Han K, Bartel CJ, et al. CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling. Nat Mach Intell. 2023;5(9):1031-41.</unstructured_citation>
						 <doi>10.1038/s42256-023-00716-3</doi> 					</citation>
          					<citation key="rk-10.68159/u729969158-2ff49145-eed6-40e6-8608-f7e784d96fe7">
					  <unstructured_citation>Yoshikawa K, Yano K, Goto S, Kim K, Matubayasi N. Graph neural network-based structural classification of glass-forming liquids and its interpretation via self-attention mechanism. J Chem Phys. 2025;163(2):024508.</unstructured_citation>
						 <doi>10.1063/5.0277279</doi> 					</citation>
          					<citation key="rk-10.68159/u729969158-95a96f05-36ff-40ec-acba-e1f15b39b136">
					  <unstructured_citation>Gurniak EJ, Yuan S, Ren X, Branicio PS. Harnessing graph convolutional neural networks for identification of glassy states in metallic glasses. Comput Mater Sci. 2024;244:113257.</unstructured_citation>
						 <doi>10.1016/j.commatsci.2024.113257</doi> 					</citation>
          					<citation key="rk-10.68159/u729969158-448eb0a3-e0ad-4638-beb6-0be50938be34">
					  <unstructured_citation>Wang Q, Zhang LF, Zhou ZY, Yu HB. Predicting the pathways of string-like motions in metallic glasses via path-featurizing graph neural networks. Sci Adv. 2024;10(21):eadk2799.</unstructured_citation>
						 <doi>10.1126/sciadv.adk2799</doi> 					</citation>
          					<citation key="rk-10.68159/u729969158-1fba0a0b-6abc-4c5c-9391-4a03f59b4b4e">
					  <unstructured_citation>Ouyang K, Zhang S, Liu SL, Tian J, Li Y, Tong H, et al. Graph learning metallic glass discovery from Wikipedia. AI Sci. 2025;1(2):025004.</unstructured_citation>
						 <doi>10.1088/3050-287X/ae1b20</doi> 					</citation>
          					<citation key="rk-10.68159/u729969158-8b6841f7-a348-4858-8dcd-1f52ea99735c">
					  <unstructured_citation>Christensen R, Smedskjær MM. Predicting dynamics from structure in a sodium silicate glass. MRS Bull. 2025;50(3):236-46.</unstructured_citation>
						 <doi>10.1557/s43577-024-00817-3</doi> 					</citation>
          					<citation key="rk-10.68159/u729969158-0240e1e0-9b94-4d57-b510-f07825a0e027">
					  <unstructured_citation>Castel N, André D, Edwards C, Evans JD, Coudert FX. Machine learning interatomic potentials for amorphous zeolitic imidazolate frameworks. Digit Discov. 2024;3(2):355-68.</unstructured_citation>
						 <doi>10.1039/D3DD00236E</doi> 					</citation>
          					<citation key="rk-10.68159/u729969158-ad3989ea-bf29-4241-8db4-48b4f7f4995d">
					  <unstructured_citation>Gramatte S, Turlo V, Politano O. Do we really need machine learning interatomic potentials for modeling amorphous metal oxides? Case study on amorphous alumina by recycling an existing ab initio database. Model Simul Mater Sci Eng. 2024;32(4):045010.</unstructured_citation>
						 <doi>10.1088/1361-651X/ad39ff</doi> 					</citation>
          					<citation key="rk-10.68159/u729969158-81e2b93f-709f-444e-b744-e19e79faf8a9">
					  <unstructured_citation>Shin HG, Kim SH, Kim EH, Gu JH, Kim J, Kim SG, et al. Charge integrated graph neural network-based machine learning potential for amorphous and non-stoichiometric hafnium oxide. npj Comput Mater. 2025;11:382.</unstructured_citation>
						 <doi>10.1038/s41524-025-01864-3</doi> 					</citation>
          					<citation key="rk-10.68159/u729969158-4ff83596-8baf-429f-b4f2-efa7c47c27a1">
					  <unstructured_citation>Ng EZX, Ang SJ, Yang H, Chen Y, Wong MW. Accelerated and efficient modeling of low-κ organosilicate glass with the M3GNet machine learning interatomic potentials. Computational Materials Today. 2025;8:100042.</unstructured_citation>
						 <doi>10.1016/j.commt.2025.100042</doi> 					</citation>
          					<citation key="rk-10.68159/u729969158-ba3383e3-5824-43a2-9586-7f7980054333">
					  <unstructured_citation>Jiang X, Tian Z, Li K. Geometry-enhanced graph neural network for learning the smoothness of glassy dynamics from static structure. arXiv Preprint. 2022. arXiv:2211.12832.</unstructured_citation>
						 <doi>10.48550/arXiv.2211.12832</doi> 					</citation>
          					<citation key="rk-10.68159/u729969158-28ce2d40-dcab-4619-a32c-cfb286653dad">
					  <unstructured_citation>Musæalian A, Batzner S, Johansson A, Sun L, Owen CJ, Kornbluth M, et al. Learning local equivariant representations for large-scale atomistic dynamics. Nat Commun. 2023;14(1):579.</unstructured_citation>
						 <doi>10.1038/s41467-023-36329-y</doi> 					</citation>
          					<citation key="rk-10.68159/u729969158-2ba52697-5ebc-4d22-b407-d6c3b538405c">
					  <unstructured_citation>Chen R, Bauchy M, Wang W, Sun Y, Tao X, Marian J. Using graph neural network and symbolic regression to model disordered systems. Sci Rep. 2025;15(1):22122.</unstructured_citation>
						 <doi>10.1038/s41598-025-05205-8</doi> 					</citation>
          					<citation key="rk-10.68159/u729969158-4afab014-0c01-4c78-a020-5e0d25a80447">
					  <unstructured_citation>Deringer VL, Caro MA, Csányi G. Machine learning interatomic potentials as emerging tools for materials science. Adv Mater. 2019;31(46):1902765.</unstructured_citation>
						 <doi>10.1002/adma.201902765</doi> 					</citation>
          					<citation key="rk-10.68159/u729969158-c49160d1-0f3f-4027-911f-db0470ecdfbc">
					  <unstructured_citation>Yang ZY, Miao Q, Dan JK, Liu MT, Wang YJ. Structural mechanism of glass transition uncovered by unsupervised machine learning. Acta Mater. 2024;281:120410.</unstructured_citation>
						 <doi>10.1016/j.actamat.2024.120410</doi> 					</citation>
          					<citation key="rk-10.68159/u729969158-957d660a-f616-4110-ab36-55d06aaa5c5a">
					  <unstructured_citation>Li R, Zhou C, Singh A, Pei Y, Henkelman G, Li L. Local-environment-guided selection of atomic structures for the development of machine-learning potentials. J Chem Phys. 2024;160(7):074109.</unstructured_citation>
						 <doi>10.1063/5.0187892</doi> 					</citation>
          					<citation key="rk-10.68159/u729969158-b5de40ee-839c-4f8d-b265-a97093004c48">
					  <unstructured_citation>Yu S, Piao X, Park N. Machine learning identifies scale-free properties in disordered materials. Nat Commun. 2020;11(1):4842.</unstructured_citation>
						 <doi>10.1038/s41467-020-18653-9</doi> 					</citation>
          					<citation key="rk-10.68159/u729969158-8829f45b-70f9-4f1a-90aa-0bc23cfb2b1f">
					  <unstructured_citation>Zhu Y, Tang YH, Kim C. Learning stochastic dynamics with statistics-informed neural network. J Comput Phys. 2023;474:111819.</unstructured_citation>
						 <doi>10.1016/j.jcp.2022.111819</doi> 					</citation>
          					<citation key="rk-10.68159/u729969158-3b727d5d-a959-4e25-b392-4f86279d4272">
					  <unstructured_citation>Bachhav B, Wu Z, Markert B, Stamm B, Shields MD, Falk ML, et al. Predicting fracture in disordered network materials using the local intelligent stress threshold indicator. Commun Phys. 2025;8(1):369.</unstructured_citation>
						 <doi>10.1038/s42005-025-02315-7</doi> 					</citation>
          					<citation key="rk-10.68159/u729969158-d69c31c2-b55b-41eb-ba9d-8cc6f475fa8f">
					  <unstructured_citation>Mao Z, Hu CS, Li J, Liang C, Das D, Sumita M, et al. Molecule graph networks with many-body equivariant interactions. J Chem Theory Comput. 2025;21(16):7954-66.</unstructured_citation>
						 <doi>10.1021/acs.jctc.5c00466</doi> 					</citation>
          					<citation key="rk-10.68159/u729969158-fb6f543a-1752-4324-8510-fd99df2687b9">
					  <unstructured_citation>Sheng Z, Zhu H, Shao B, He Y, Liu Z, Wang S, et al. Accelerated discovery of energy materials via graph neural network. Inorganics. 2025;13(12):395.</unstructured_citation>
						 <doi>10.3390/inorganics13120395</doi> 					</citation>
          					<citation key="rk-10.68159/u729969158-3eb264a4-6c1e-4bfe-8d3d-44c293f685ac">
					  <unstructured_citation>Chong S, Grasselli F, Ben Mahmoud C, Morrow JD, Deringer VL, Ceriotti M. Robustness of local predictions in atomistic machine learning models. J Chem Theory Comput. 2023;19(22):8020-31.</unstructured_citation>
						 <doi>10.1021/acs.jctc.3c00704</doi> 					</citation>
          					<citation key="rk-10.68159/u729969158-04a033e6-af4d-44b1-9cb7-0cc41176b45a">
					  <unstructured_citation>Jerng SE. Path to machine learning-driven autonomous systems for solid-state electrolyte batteries: Design, fabrication, and lifetime prediction. ACS Appl Energy Mater. 2025;8(20):14971-86.</unstructured_citation>
						 <doi>10.1021/acsaem.5c02002</doi> 					</citation>
          					<citation key="rk-10.68159/u729969158-56094c38-f437-4fa6-9272-f15ed9ba2db9">
					  <unstructured_citation>Somnic J, Jo BW. Status and challenges in homogenization methods for lattice materials. Materials (Basel). 2022;15(2):605.</unstructured_citation>
						 <doi>10.3390/ma15020605</doi> 					</citation>
          					<citation key="rk-10.68159/u729969158-40f7f7ef-2ff0-42e7-b753-d36cb1a9b267">
					  <unstructured_citation>Grega I, Batatia I, Indurkar PP, Csányi G, Karlapati S, Deshpande VS. Graph neural networks for strut-based architected solids. J Mech Phys Solids. 2025;195:105966.</unstructured_citation>
						 <doi>10.1016/j.jmps.2024.105966</doi> 					</citation>
          				</citation_list>
			</journal_article>
		</journal>
	</body>
</doi_batch>
