{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T19:53:44Z","timestamp":1776455624586,"version":"3.51.2"},"reference-count":67,"publisher":"IOP Publishing","issue":"1","license":[{"start":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T00:00:00Z","timestamp":1738108800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T00:00:00Z","timestamp":1738108800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2020YFA0713504"],"award-info":[{"award-number":["2020YFA0713504"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62101572"],"award-info":[{"award-number":["62101572"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Research Program of National University of Defense Technology","award":["ZK21-16"],"award-info":[{"award-number":["ZK21-16"]}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Mach. Learn.: Sci. Technol."],"published-print":{"date-parts":[[2025,3,31]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The prediction of material properties is a crucial challenge in the design of new materials. Traditional methods based on either trial-and-error experiments or large-scale density functional theory calculations are known to possess various limitations. Although recent machine learning (ML) methods have shed light on resolving this problem efficiently, the majority of ML models consider only the local atomic environment while ignoring the nonlocal correlations between atoms. Indeed, even the periodic patterns of the crystal structures are not seriously considered. Consequently, these issues lead to an insufficient understanding of the feature information of atoms and bonds. In this study, we propose a crystal graph convolutional neural network based on edge convolution (EdgeConv) and correlative self-attention, namely, EdgeConv-Graph attention neural network (GANN). This network is able to efficiently extract atomic and bonding feature information, while effectively learning the importance weights of all neighbouring nodes. Numerical experiments predicting the electronic structural properties of metal\u2013organic frameworks show that the developed model achieves state-of-the-art performance. Moreover, the proposed model was applied to predict the heat capacity and thermal decomposition temperature of material, demonstrating the ability of this method to effectively generalise multiscale prediction tasks.<\/jats:p>","DOI":"10.1088\/2632-2153\/ad9fcf","type":"journal-article","created":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T23:03:46Z","timestamp":1734390226000},"page":"015020","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Incorporating edge convolution and correlative self-attention into graph neural network for material properties prediction"],"prefix":"10.1088","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-6997-2125","authenticated-orcid":true,"given":"Zexi","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5401-3494","authenticated-orcid":true,"given":"Qi","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-1857-1643","authenticated-orcid":true,"given":"Yapeng","family":"Zhan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0337-588X","authenticated-orcid":true,"given":"Jiying","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2025,1,29]]},"reference":[{"key":"mlstad9fcfbib1","doi-asserted-by":"publisher","first-page":"B864","DOI":"10.1103\/PhysRev.136.B864","article-title":"Inhomogeneous electron gas","volume":"136","author":"Hohenberg","year":"1964","journal-title":"Phys. Rev."},{"key":"mlstad9fcfbib2","doi-asserted-by":"publisher","first-page":"A1133","DOI":"10.1103\/PhysRev.140.A1133","article-title":"Self-consistent equations including exchange and correlation effects","volume":"140","author":"Kohn","year":"1965","journal-title":"Phys. Rev."},{"key":"mlstad9fcfbib3","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1038\/s41586-018-0337-2","article-title":"Machine learning for molecular and materials science","volume":"559","author":"Butler","year":"2018","journal-title":"Nature"},{"key":"mlstad9fcfbib4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41524-023-01000-z","article-title":"Small data machine learning in materials science","volume":"9","author":"Xu","year":"2023","journal-title":"npj Comput. Mater."},{"key":"mlstad9fcfbib5","doi-asserted-by":"publisher","first-page":"1003","DOI":"10.1007\/s12613-022-2595-0","article-title":"Advances in machine learning- and artificial intelligence-assisted material design of steels","volume":"30","author":"Pan","year":"2023","journal-title":"Int. J. Miner. Metall. Mater."},{"key":"mlstad9fcfbib6","doi-asserted-by":"publisher","first-page":"1042","DOI":"10.1007\/s40843-024-2851-9","article-title":"Methods and applications of machine learning in computational design of optoelectronic semiconductors","volume":"67","author":"Yang","year":"2024","journal-title":"Sci. China Mater."},{"key":"mlstad9fcfbib7","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1088\/1361-651X\/ad2540","article-title":"Exploring thermal properties of PbSnTeSe and PbSnTeS high entropy alloys with machine-learned potentials","volume":"32","author":"Chang","year":"2024","journal-title":"Modelling Simul. Mater. Sci. Eng."},{"key":"mlstad9fcfbib8","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1038\/s42256-020-00271-1","article-title":"Inverse design of nanoporous crystalline reticular materials with deep generative models","volume":"3","author":"Yao","year":"2021","journal-title":"Nat. Mach. Intell."},{"key":"mlstad9fcfbib9","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1002\/adfm.202003619","article-title":"Multiscale construction of bifunctional electrocatalysts for long\u2010lifespan rechargeable zinc\u2013air batteries","volume":"30","author":"Zhao","year":"2020","journal-title":"Adv. Funct. Mater."},{"key":"mlstad9fcfbib10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-020-15619-9","article-title":"Quantitative prediction of grain boundary thermal conductivities from local atomic environments","volume":"11","author":"Fujii","year":"2020","journal-title":"Nat. Commun."},{"key":"mlstad9fcfbib11","doi-asserted-by":"publisher","first-page":"5209","DOI":"10.1039\/C9TA12608B","article-title":"Machine learning-based high throughput screening for nitrogen fixation on boron-doped single atom catalysts","volume":"8","author":"Zafari","year":"2020","journal-title":"J. Mater. Chem. A"},{"key":"mlstad9fcfbib12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/ncomms15679","article-title":"Universal fragment descriptors for predicting properties of inorganic crystals","volume":"8","author":"Isayev","year":"2017","journal-title":"Nat. Commun."},{"key":"mlstad9fcfbib13","doi-asserted-by":"publisher","first-page":"735","DOI":"10.1021\/cm503507h","article-title":"Materials cartography: representing and mining materials space using structural and electronic fingerprints","volume":"27","author":"Isayev","year":"2015","journal-title":"Chem. Mater."},{"key":"mlstad9fcfbib14","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1103\/PhysRevB.95.144110","article-title":"Representation of compounds for machine-learning prediction of physical properties","volume":"95","author":"Seko","year":"2017","journal-title":"Phys. Rev. B"},{"key":"mlstad9fcfbib15","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1103\/PhysRevLett.104.136403","article-title":"Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons","volume":"104","author":"Bart\u00f3k","year":"2010","journal-title":"Phys. Rev. Lett."},{"key":"mlstad9fcfbib16","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1103\/PhysRevB.87.184115","article-title":"On representing chemical environments","volume":"87","author":"Bart\u00f3k","year":"2013","journal-title":"Phys. Rev. B"},{"key":"mlstad9fcfbib17","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1063\/1.3553717","article-title":"Atom-centered symmetry functions for constructing high-dimensional neural network potentials","volume":"134","author":"Behler","year":"2011","journal-title":"J. Chem. Phys."},{"key":"mlstad9fcfbib18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-021-04305-2","article-title":"Co-AMPpred for in silico-aided predictions of antimicrobial peptides by integrating composition-based features","volume":"22","author":"Singh","year":"2021","journal-title":"BMC Bioinf."},{"key":"mlstad9fcfbib19","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1002\/advs.202302508","article-title":"A comprehensive and versatile multimodal deep\u2010learning approach for predicting diverse properties of advanced materials","volume":"10","author":"Muroga","year":"2023","journal-title":"Adv. Sci."},{"key":"mlstad9fcfbib20","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1063\/1.5019779","article-title":"SchNet\u2014a deep learning architecture for molecules and materials","volume":"148","author":"Sch\u00fctt","year":"2018","journal-title":"J. Chem. Phys."},{"key":"mlstad9fcfbib21","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1103\/PhysRevLett.120.145301","article-title":"Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties","volume":"120","author":"Xie","year":"2018","journal-title":"Phys. Rev. Lett."},{"key":"mlstad9fcfbib22","doi-asserted-by":"publisher","first-page":"18141","DOI":"10.1039\/D0CP01474E","article-title":"Graph convolutional neural networks with global attention for improved materials property prediction","volume":"22","author":"Louis","year":"2020","journal-title":"Phys. Chem. Chem. Phys."},{"key":"mlstad9fcfbib23","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1088\/1361-648X\/ad2584","article-title":"Graph attention neural networks for mapping materials and molecules beyond short-range interatomic correlations","volume":"36","author":"Liu","year":"2024","journal-title":"J. Phys."},{"key":"mlstad9fcfbib24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-018-35934-y","article-title":"ElemNet: deep learning the chemistry of materials from only elemental composition","volume":"8","author":"Jha","year":"2018","journal-title":"Sci. Rep."},{"key":"mlstad9fcfbib25","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1007\/s40192-021-00247-y","article-title":"CrabNet for explainable deep learning in materials science: bridging the gap between academia and industry","volume":"11","author":"Wang","year":"2022","journal-title":"Integr. Mater. Manuf. Innov."},{"key":"mlstad9fcfbib26","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1038\/s43588-020-00002-x","article-title":"Learning properties of ordered and disordered materials from multi-fidelity data","volume":"1","author":"Chen","year":"2021","journal-title":"Nat. Comput. Sci."},{"key":"mlstad9fcfbib27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41524-021-00554-0","article-title":"Benchmarking graph neural networks for materials chemistry","volume":"7","author":"Fung","year":"2021","journal-title":"npj Comput. Mater."},{"key":"mlstad9fcfbib28","doi-asserted-by":"publisher","first-page":"3168","DOI":"10.1103\/PhysRevLett.76.3168","article-title":"Density functional and density matrix method scaling linearly with the number of atoms","volume":"76","author":"Kohn","year":"1996","journal-title":"Phys. Rev. Lett."},{"key":"mlstad9fcfbib29","doi-asserted-by":"publisher","first-page":"11635","DOI":"10.1073\/pnas.0505436102","article-title":"Nearsightedness of electronic matter","volume":"102","author":"Prodan","year":"2005","journal-title":"Proc. Natl Acad. Sci."},{"key":"mlstad9fcfbib30","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1038\/s43588-022-00265-6","article-title":"Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation","volume":"2","author":"Li","year":"2022","journal-title":"Nat. Comput. Sci."},{"key":"mlstad9fcfbib31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-020-20427-2","article-title":"A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer","volume":"12","author":"Ko","year":"2021","journal-title":"Nat. Commun."},{"key":"mlstad9fcfbib32","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1140\/epjb\/s10051-021-00156-1","article-title":"Machine learning potentials for extended systems: a perspective","volume":"94","author":"Behler","year":"2021","journal-title":"Eur. Phys. J. B"},{"key":"mlstad9fcfbib33","doi-asserted-by":"publisher","first-page":"1578","DOI":"10.1016\/j.matt.2021.02.015","article-title":"Machine learning the quantum-chemical properties of metal\u2013organic frameworks for accelerated materials discovery","volume":"4","author":"Rosen","year":"2021","journal-title":"Matter"},{"key":"mlstad9fcfbib34","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1039\/c0cp02394a","article-title":"Catalysis by metal\u2013organic frameworks: fundamentals and opportunities","volume":"13","author":"Ranocchiari","year":"2011","journal-title":"Phys. Chem. Chem. Phys."},{"key":"mlstad9fcfbib35","doi-asserted-by":"publisher","first-page":"8134","DOI":"10.1039\/C8CS00256H","article-title":"Catalysis and photocatalysis by metal organic frameworks","volume":"47","author":"Dhakshinamoorthy","year":"2018","journal-title":"Chem. Soc. Rev."},{"key":"mlstad9fcfbib36","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1016\/j.jece.2022.108300","article-title":"Progress and potential of metal-organic frameworks (MOFs) for gas storage and separation: a review","volume":"10","author":"Jia","year":"2022","journal-title":"J. Environ. Chem. Eng."},{"key":"mlstad9fcfbib37","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1016\/j.mattod.2017.07.006","article-title":"Recent advances in gas storage and separation using metal\u2013organic frameworks","volume":"21","author":"Li","year":"2018","journal-title":"Mater. Today"},{"key":"mlstad9fcfbib38","doi-asserted-by":"publisher","first-page":"3185","DOI":"10.1039\/c7cs00122c","article-title":"An updated roadmap for the integration of metal\u2013organic frameworks with electronic devices and chemical sensors","volume":"46","author":"Stassen","year":"2017","journal-title":"Chem. Soc. Rev."},{"key":"mlstad9fcfbib39","doi-asserted-by":"publisher","first-page":"15192","DOI":"10.1002\/anie.202006402","article-title":"The role of metal\u2013organic frameworks in electronic sensors","volume":"60","author":"Zhang","year":"2021","journal-title":"Angew. Chem., Int. Ed."},{"key":"mlstad9fcfbib40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41597-023-02116-z","article-title":"CRAFTED: an exploratory database of simulated adsorption isotherms of metal-organic frameworks","volume":"10","author":"Oliveira","year":"2023","journal-title":"Sci. Data"},{"key":"mlstad9fcfbib41","volume":"vol 2","author":"West","year":"2001"},{"key":"mlstad9fcfbib42","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2021.103113","article-title":"Exploring an edge convolution and normalization based approach for link prediction in complex networks","volume":"189","author":"Zhang","year":"2021","journal-title":"J. Netw. Comput. Appl."},{"key":"mlstad9fcfbib43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3326362","article-title":"Dynamic graph CNN for learning on point clouds","volume":"38","author":"Wang","year":"2019","journal-title":"ACM Trans. Graph."},{"key":"mlstad9fcfbib44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-023-50600-8","article-title":"Incorporating high-frequency information into edge convolution for link prediction in complex networks","volume":"14","author":"Zhang","year":"2024","journal-title":"Sci. Rep."},{"key":"mlstad9fcfbib45","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1007\/s41095-021-0229-5","article-title":"Pct: point cloud transformer","volume":"7","author":"Guo","year":"2021","journal-title":"Comput. Vis. Media"},{"key":"mlstad9fcfbib46","first-page":"1243","article-title":"AM-GCN: adaptive multi-channel graph convolutional networks","author":"Wang","year":"2020"},{"key":"mlstad9fcfbib47","first-page":"315","article-title":"Sclip: rethinking self-attention for dense vision-language inference","author":"Wang","year":"2025"},{"key":"mlstad9fcfbib48","first-page":"3828","article-title":"Grounding everything: emerging localization properties in vision-language transformers","author":"Bousselham","year":"2024"},{"key":"mlstad9fcfbib49","doi-asserted-by":"publisher","first-page":"3865","DOI":"10.1103\/PhysRevLett.77.3865","article-title":"Generalized gradient approximation made simple","volume":"77","author":"Perdew","year":"1996","journal-title":"Phys. Rev. Lett."},{"key":"mlstad9fcfbib50","doi-asserted-by":"publisher","first-page":"1456","DOI":"10.1002\/jcc.21759","article-title":"Effect of the damping function in dispersion corrected density functional theory","volume":"32","author":"Grimme","year":"2011","journal-title":"J. Comput. Chem."},{"key":"mlstad9fcfbib51","doi-asserted-by":"publisher","first-page":"7411","DOI":"10.1021\/jacs.8b03604","article-title":"Tunable mixed-valence doping toward record electrical conductivity in a three-dimensional metal\u2013organic framework","volume":"140","author":"Xie","year":"2018","journal-title":"J. Am. Chem. Soc."},{"key":"mlstad9fcfbib52","doi-asserted-by":"publisher","first-page":"3566","DOI":"10.1002\/anie.201506219","article-title":"Electrically conductive porous metal\u2013organic frameworks","volume":"55","author":"Sun","year":"2016","journal-title":"Angew. Chem., Int. Ed."},{"key":"mlstad9fcfbib53","doi-asserted-by":"publisher","first-page":"1419","DOI":"10.1038\/s41563-022-01374-3","article-title":"A data-science approach to predict the heat capacity of nanoporous materials","volume":"21","author":"Moosavi","year":"2022","journal-title":"Nat. Mater."},{"key":"mlstad9fcfbib54","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41597-022-01181-0","article-title":"MOFSimplify, machine learning models with extracted stability data of three thousand metal\u2013organic frameworks","volume":"9","author":"Nandy","year":"2022","journal-title":"Sci. Data"},{"key":"mlstad9fcfbib55","doi-asserted-by":"publisher","first-page":"1094","DOI":"10.1002\/qua.24917","article-title":"Crystal structure representations for machine learning models of formation energies","volume":"115","author":"Faber","year":"2015","journal-title":"Int. J. Quantum Chem."},{"key":"mlstad9fcfbib56","doi-asserted-by":"publisher","first-page":"4562","DOI":"10.1021\/acs.jpclett.8b01707","article-title":"Metallic metal\u2013organic frameworks predicted by the combination of machine learning methods and ab initio calculations","volume":"9","author":"He","year":"2018","journal-title":"J. Phys. Chem. Lett."},{"key":"mlstad9fcfbib57","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1103\/PhysRevB.89.094104","article-title":"Combinatorial screening for new materials in unconstrained composition space with machine learning","volume":"89","author":"Meredig","year":"2014","journal-title":"Phys. Rev. B"},{"key":"mlstad9fcfbib58","doi-asserted-by":"publisher","first-page":"756","DOI":"10.1080\/14686996.2017.1378060","article-title":"Machine learning reveals orbital interaction in materials","volume":"18","author":"Pham","year":"2017","journal-title":"Sci. Technol. Adv. Mater."},{"key":"mlstad9fcfbib59","doi-asserted-by":"publisher","DOI":"10.1016\/j.cpc.2019.106949","article-title":"DScribe: library of descriptors for machine learning in materials science","volume":"247","author":"Himanen","year":"2020","journal-title":"Comput. Phys. Commun."},{"key":"mlstad9fcfbib60","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41597-022-01294-6","article-title":"Auto-generated database of semiconductor band gaps using ChemDataExtractor","volume":"9","author":"Dong","year":"2022","journal-title":"Sci. Data"},{"key":"mlstad9fcfbib61","doi-asserted-by":"publisher","first-page":"2432","DOI":"10.1016\/j.ijleo.2012.07.024","article-title":"Crystallographic and microscopic properties of ternary CdS0.5Se0.5 thin films","volume":"124","author":"Khomane","year":"2013","journal-title":"Optik"},{"key":"mlstad9fcfbib62","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1063\/1.555642","article-title":"Heat capacity and other thermodynamic properties of linear macromolecules I. Selenium","volume":"10","author":"Gaur","year":"1981","journal-title":"J. Phys. Chem. Ref. Data"},{"key":"mlstad9fcfbib63","first-page":"347","article-title":"Microscopic-macroscopic relationships in silicates","author":"Geiger","year":"2002"},{"key":"mlstad9fcfbib64","doi-asserted-by":"publisher","first-page":"17535","DOI":"10.1021\/jacs.1c07217","article-title":"Using machine learning and data mining to leverage community knowledge for the engineering of stable metal\u2013organic frameworks","volume":"143","author":"Nandy","year":"2021","journal-title":"J. Am. Chem. Soc."},{"key":"mlstad9fcfbib65","doi-asserted-by":"publisher","first-page":"15788","DOI":"10.1021\/acsami.9b02764","article-title":"Torsion angle effect on the activation of UiO metal\u2013organic frameworks","volume":"11","author":"Ayoub","year":"2019","journal-title":"ACS Appl. Mater. Interfaces"},{"key":"mlstad9fcfbib66","doi-asserted-by":"publisher","first-page":"10283","DOI":"10.1021\/jacs.9b02947","article-title":"Ligand rigidification for enhancing the stability of metal\u2013organic frameworks","volume":"141","author":"Lv","year":"2019","journal-title":"J. Am. Chem. Soc."},{"key":"mlstad9fcfbib67","doi-asserted-by":"publisher","first-page":"9244","DOI":"10.5555\/3454287.3455116","article-title":"Gnnexplainer: generating explanations for graph neural networks","volume":"32","author":"Ying","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."}],"container-title":["Machine Learning: Science and Technology"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad9fcf","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad9fcf\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad9fcf","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad9fcf\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad9fcf\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad9fcf\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad9fcf\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad9fcf\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T11:25:03Z","timestamp":1738149903000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad9fcf"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,29]]},"references-count":67,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,1,29]]},"published-print":{"date-parts":[[2025,3,31]]}},"URL":"https:\/\/doi.org\/10.1088\/2632-2153\/ad9fcf","relation":{},"ISSN":["2632-2153"],"issn-type":[{"value":"2632-2153","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,29]]},"assertion":[{"value":"Incorporating edge convolution and correlative self-attention into graph neural network for material properties prediction","name":"article_title","label":"Article Title"},{"value":"Machine Learning: Science and Technology","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2025 The Author(s). Published by IOP Publishing Ltd","name":"copyright_information","label":"Copyright Information"},{"value":"2024-09-11","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2024-12-16","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-01-29","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}