{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T07:20:36Z","timestamp":1769152836787,"version":"3.49.0"},"reference-count":66,"publisher":"IOP Publishing","issue":"2","license":[{"start":{"date-parts":[[2024,5,3]],"date-time":"2024-05-03T00:00:00Z","timestamp":1714694400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,5,3]],"date-time":"2024-05-03T00:00:00Z","timestamp":1714694400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100006116","name":"Advanced Manufacturing Office","doi-asserted-by":"crossref","award":["Critical Materials Institute"],"award-info":[{"award-number":["Critical Materials Institute"]}],"id":[{"id":"10.13039\/100006116","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000181","name":"Air Force Office of Scientific Research","doi-asserted-by":"crossref","award":["FA9550-21-1-0460"],"award-info":[{"award-number":["FA9550-21-1-0460"]}],"id":[{"id":"10.13039\/100000181","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100006151","name":"Basic Energy Sciences","doi-asserted-by":"crossref","award":["DE-SC0019111"],"award-info":[{"award-number":["DE-SC0019111"]}],"id":[{"id":"10.13039\/100006151","id-type":"DOI","asserted-by":"crossref"}]},{"name":"National Science Foundation","award":["CMMI-1929949"],"award-info":[{"award-number":["CMMI-1929949"]}]},{"DOI":"10.13039\/100006227","name":"Lawrence Livermore National Laboratory","doi-asserted-by":"crossref","award":["LDRD 22-ERD-016"],"award-info":[{"award-number":["LDRD 22-ERD-016"]}],"id":[{"id":"10.13039\/100006227","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Mach. Learn.: Sci. Technol."],"published-print":{"date-parts":[[2024,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Surrogate models driven by sizeable datasets and scientific machine-learning methods have emerged as an attractive microstructure simulation tool with the potential to deliver predictive microstructure evolution dynamics with huge savings in computational costs. Taking 2D and 3D grain growth simulations as an example, we present a completely overhauled computational framework based on graph neural networks with not only excellent agreement to both the ground truth phase-field methods and theoretical predictions, but enhanced accuracy and efficiency compared to previous works based on convolutional neural networks. These improvements can be attributed to the graph representation, both improved predictive power and a more flexible data structure amenable to adaptive mesh refinement. As the simulated microstructures coarsen, our method can adaptively adopt remeshed grids and larger timesteps to achieve further speedup. The data-to-model pipeline with training procedures together with the source codes are provided.<\/jats:p>","DOI":"10.1088\/2632-2153\/ad3e4b","type":"journal-article","created":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T22:41:05Z","timestamp":1712961665000},"page":"025027","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Accelerate microstructure evolution simulation using graph neural networks with adaptive spatiotemporal resolution"],"prefix":"10.1088","volume":"5","author":[{"given":"Shaoxun","family":"Fan","sequence":"first","affiliation":[]},{"given":"Andrew L","family":"Hitt","sequence":"additional","affiliation":[]},{"given":"Ming","family":"Tang","sequence":"additional","affiliation":[]},{"given":"Babak","family":"Sadigh","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9659-4648","authenticated-orcid":true,"given":"Fei","family":"Zhou","sequence":"additional","affiliation":[]}],"member":"266","published-online":{"date-parts":[[2024,5,3]]},"reference":[{"key":"mlstad3e4bbib1","doi-asserted-by":"publisher","first-page":"R4092","DOI":"10.1103\/PhysRevE.58.R4092","article-title":"Three-dimensional simulations of ostwald ripening with elastic effects","volume":"58","author":"Sagui","year":"1998","journal-title":"Phys. Rev. E"},{"key":"mlstad3e4bbib2","doi-asserted-by":"publisher","first-page":"6119","DOI":"10.1103\/PhysRevB.31.6119","article-title":"Diffuse interface model of diffusion-limited crystal growth","volume":"31","author":"Collins","year":"1985","journal-title":"Phys. Rev. B"},{"key":"mlstad3e4bbib3","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/0167-2789(95)00298-7","article-title":"A phase field concept for multiphase systems","volume":"94","author":"Steinbach","year":"1996","journal-title":"Physica D"},{"key":"mlstad3e4bbib4","doi-asserted-by":"publisher","first-page":"980","DOI":"10.1103\/PhysRevB.42.980","article-title":"Pattern formation in phase-separating alloys with cubic symmetry","volume":"42","author":"Nishimori","year":"1990","journal-title":"Phys. Rev. B"},{"key":"mlstad3e4bbib5","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/j.commatsci.2017.09.029","article-title":"Quantitative 3d phase field modelling of solidification using next-generation adaptive mesh refinement","volume":"142","author":"Greenwood","year":"2018","journal-title":"Comput. Mater. Sci."},{"key":"mlstad3e4bbib6","doi-asserted-by":"publisher","first-page":"6135","DOI":"10.1016\/j.jcp.2010.04.045","article-title":"Three-dimensional, fully adaptive simulations of phase-field fluid models","volume":"229","author":"Ceniceros","year":"2010","journal-title":"J. Comput. Phys."},{"key":"mlstad3e4bbib7","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.cpc.2015.01.016","article-title":"On solving the 3-d phase field equations by employing a parallel-adaptive mesh refinement (para-AMR) algorithm","volume":"190","author":"Guo","year":"2015","journal-title":"Comput. Phys. Commun."},{"key":"mlstad3e4bbib8","doi-asserted-by":"publisher","DOI":"10.1088\/0965-0393\/24\/7\/075005","article-title":"A Markov random field approach for modeling spatio-temporal evolution of microstructures","volume":"24","author":"Acar","year":"2016","journal-title":"Modelling Simul. Mater. Sci. Eng."},{"key":"mlstad3e4bbib9","doi-asserted-by":"publisher","DOI":"10.1016\/j.cad.2019.102806","article-title":"Polycrystalline Microstructure Reconstruction Using Markov Random Fields and Histogram Matching","volume":"120","author":"Javaheri","year":"2020","journal-title":"Comput. Aided Des."},{"key":"mlstad3e4bbib10","doi-asserted-by":"publisher","DOI":"10.1016\/j.commatsci.2022.111228","article-title":"Large-scale synthesis of metal additively-manufactured microstructures using Markov random fields","volume":"206","author":"Javaheri","year":"2022","journal-title":"Comput. Mater. Sci."},{"key":"mlstad3e4bbib11","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":"mlstad3e4bbib12","article-title":"Artificial intelligence for science in quantum, atomistic, and continuum systems","author":"Zhang","year":"2023"},{"key":"mlstad3e4bbib13","doi-asserted-by":"publisher","DOI":"10.1016\/j.mtcomm.2022.103174","article-title":"Synergy of unsupervised and supervised machine learning methods for the segmentation of the graphite particles in the microstructure of ductile iron","volume":"30","author":"Alrfou","year":"2022","journal-title":"Mater. Today Commun."},{"key":"mlstad3e4bbib14","doi-asserted-by":"publisher","first-page":"3466","DOI":"10.1007\/s11837-022-05265-5","article-title":"Feature extraction and microstructural classification of hot stamping ultra-high strength steel by machine learning","volume":"74","author":"Zhu","year":"2022","journal-title":"JOM"},{"key":"mlstad3e4bbib15","doi-asserted-by":"publisher","DOI":"10.5281\/zenodo.6924532)","article-title":"Sciencedirect three-dimensional microstructure generation using generative adversarial neural networks in the context of continuum micromechanics","author":"Henkes","year":"2022"},{"key":"mlstad3e4bbib16","doi-asserted-by":"publisher","DOI":"10.1016\/j.matdes.2023.111775","article-title":"A new method for classifying and segmenting material microstructure based on machine learning","volume":"227","author":"Zhao","year":"2023","journal-title":"Mater. Design"},{"key":"mlstad3e4bbib17","doi-asserted-by":"publisher","DOI":"10.1016\/j.actamat.2023.119106","article-title":"Machine learning based quantitative characterization of microstructures","volume":"256","author":"Gorynski","year":"2023","journal-title":"Acta Mater."},{"key":"mlstad3e4bbib18","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1038\/s41524-021-00568-8","article-title":"Super-resolving material microstructure image via deep learning for microstructure characterization and mechanical behavior analysis","volume":"7","author":"Jung","year":"2021","journal-title":"npj Comput. Mater."},{"key":"mlstad3e4bbib19","doi-asserted-by":"publisher","DOI":"10.1016\/j.commatsci.2021.110576","article-title":"Multi defect detection and analysis of electron microscopy images with deep learning","volume":"199","author":"Shen","year":"2021","journal-title":"Comput. Mater. Sci."},{"key":"mlstad3e4bbib20","doi-asserted-by":"publisher","DOI":"10.3389\/fmats.2023.1086000","article-title":"Automated, high-accuracy classification of textured microstructures using a convolutional neural network","volume":"10","author":"Khurjekar","year":"2023","journal-title":"Front. Mater."},{"key":"mlstad3e4bbib21","doi-asserted-by":"publisher","DOI":"10.1016\/j.patter.2021.100243","article-title":"Self-supervised learning and prediction of microstructure evolution with convolutional recurrent neural networks","volume":"2","author":"Yang","year":"2021","journal-title":"Patterns"},{"key":"mlstad3e4bbib22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41524-020-00471-8","article-title":"Accelerating phase-field-based microstructure evolution predictions via surrogate models trained by machine learning methods","volume":"7","author":"Montes de Oca Zapiain","year":"2021","journal-title":"npj Comput. Mater."},{"key":"mlstad3e4bbib23","doi-asserted-by":"publisher","DOI":"10.1016\/j.commatsci.2023.112187","article-title":"Emulating microstructural evolution during spinodal decomposition using a tensor decomposed convolutional and recurrent neural network","volume":"224","author":"Wu","year":"2023","journal-title":"Comput. Mater. Sci."},{"key":"mlstad3e4bbib24","doi-asserted-by":"publisher","DOI":"10.1016\/j.commatsci.2023.112110","article-title":"Spatiotemporal prediction of microstructure evolution with predictive recurrent neural network","volume":"223","author":"Kazemzadeh Farizhandi","year":"2023","journal-title":"Comput. Mater. Sci."},{"key":"mlstad3e4bbib25","article-title":"Eidetic 3D LSTM: a model for video prediction and beyond","author":"Wang","year":"2019"},{"key":"mlstad3e4bbib26","article-title":"Relational inductive biases, deep learning, and graph networks","author":"Battaglia","year":"2018"},{"key":"mlstad3e4bbib27","article-title":"Learning to simulate complex physics with graph networks","author":"Sanchez-Gonzalez","year":"2020"},{"key":"mlstad3e4bbib28","doi-asserted-by":"publisher","DOI":"10.26434\/chemrxiv-2023-wl8rn)","article-title":"Sli-gnn: a self-learning-input graph neural network for predicting crystal and molecular properties","author":"Dong","year":"2023"},{"key":"mlstad3e4bbib29","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1038\/s43246-021-00194-3","article-title":"A geometric-information-enhanced crystal graph network for predicting properties of materials","volume":"2","author":"Cheng","year":"2021","journal-title":"Commun. Mater."},{"key":"mlstad3e4bbib30","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1038\/s41524-021-00574-w","article-title":"Graph neural networks for an accurate and interpretable prediction of the properties of polycrystalline materials","volume":"7","author":"Dai","year":"2021","journal-title":"npj Comput. Mater."},{"key":"mlstad3e4bbib31","doi-asserted-by":"publisher","first-page":"2453","DOI":"10.1038\/s41467-022-29939-5","article-title":"E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials","volume":"13","author":"Batzner","year":"2022","journal-title":"Nat. Commun."},{"key":"mlstad3e4bbib32","doi-asserted-by":"publisher","first-page":"718","DOI":"10.1038\/s43588-022-00349-3","article-title":"A universal graph deep learning interatomic potential for the periodic table","volume":"2","author":"Chen","year":"2022","journal-title":"Nat. Comput. Sci."},{"key":"mlstad3e4bbib33","doi-asserted-by":"publisher","DOI":"10.1063\/5.0083060","article-title":"Graph neural networks accelerated molecular dynamics","volume":"156","author":"Li","year":"2022","journal-title":"J. Chem. Phys."},{"key":"mlstad3e4bbib34","article-title":"Learning mesh-based simulation with graph networks","author":"Pfaff","year":"2020"},{"key":"mlstad3e4bbib35","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2023.112180","article-title":"Accelerating discrete dislocation dynamics simulations with graph neural networks","volume":"487","author":"Bertin","year":"2023","journal-title":"J. Comput. Phys."},{"key":"mlstad3e4bbib36","article-title":"Learning dislocation dynamics mobility laws from large-scale MD simulations","author":"Bertin","year":"2023"},{"key":"mlstad3e4bbib37","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1038\/s41524-022-00890-9","article-title":"Physics-embedded graph network for accelerating phase-field simulation of microstructure evolution in additive manufacturing","volume":"8","author":"Xue","year":"2022","journal-title":"npj Comput. Mater."},{"key":"mlstad3e4bbib38","doi-asserted-by":"publisher","DOI":"10.1016\/j.commatsci.2022.111927","article-title":"GrainNN: a neighbor-aware long short-term memory network for predicting microstructure evolution during polycrystalline grain formation","volume":"218","author":"Qin","year":"2023","journal-title":"Comput. Mater. Sci."},{"key":"mlstad3e4bbib39","volume":"vol 108","author":"Von Neumann","year":"1952"},{"key":"mlstad3e4bbib40","doi-asserted-by":"publisher","first-page":"900","DOI":"10.1063\/1.1722511","article-title":"Two-dimensional motion of idealized grain boundaries","volume":"27","author":"Mullins","year":"1956","journal-title":"J. Appl. Phys."},{"key":"mlstad3e4bbib41","doi-asserted-by":"publisher","first-page":"3057","DOI":"10.1016\/S1359-6454(02)00084-8","article-title":"Computer simulation of 3d grain growth using a phase-field model","volume":"50","author":"Krill","year":"2002","journal-title":"Acta Mater."},{"key":"mlstad3e4bbib42","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.74.061605","article-title":"Computer simulations of two-dimensional and three-dimensional ideal grain growth","volume":"74","author":"Kim","year":"2006","journal-title":"Phys. Rev. E"},{"key":"mlstad3e4bbib43","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1016\/j.calphad.2007.11.003","article-title":"An introduction to phase-field modeling of microstructure evolution","volume":"32","author":"Moelans","year":"2008","journal-title":"Calphad"},{"key":"mlstad3e4bbib44","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1016\/j.commatsci.2009.03.037","article-title":"Comparative study of two phase-field models for grain growth","volume":"46","author":"Moelans","year":"2009","journal-title":"Comput. Mater. Sci."},{"key":"mlstad3e4bbib45","doi-asserted-by":"publisher","first-page":"1697","DOI":"10.1016\/j.scriptamat.2005.12.042","article-title":"Three-dimensional normal grain growth: Monte Carlo Potts model simulation and analytical mean field theory","volume":"54","author":"Z\u00f6llner","year":"2006","journal-title":"Scr. Mater."},{"key":"mlstad3e4bbib46","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1016\/j.msea.2006.06.115","article-title":"A 3-D Monte-Carlo (Potts) model for recrystallization and grain growth in polycrystalline materials","volume":"433","author":"Ivasishin","year":"2006","journal-title":"Mater. Sci. Eng. A"},{"key":"mlstad3e4bbib47","doi-asserted-by":"publisher","DOI":"10.1088\/0965-0393\/20\/7\/075009","article-title":"Evaluating microstructural parameters of three-dimensional grains generated by phase-field simulation or other voxel-based techniques","volume":"20","author":"Chang","year":"2012","journal-title":"Modelling Simul. Mater. Sci. Eng."},{"key":"mlstad3e4bbib48","doi-asserted-by":"publisher","first-page":"1681","DOI":"10.1016\/1359-6454(95)00262-6","article-title":"The influence of spatial grain size correlation and topology on normal grain growth in two dimensions","volume":"44","author":"Marthinsen","year":"1996","journal-title":"Acta Mater."},{"key":"mlstad3e4bbib49","doi-asserted-by":"publisher","first-page":"1297","DOI":"10.1016\/S1359-6454(99)00405-X","article-title":"Three-dimensional microstructural evolution in ideal grain growth general statistics","volume":"48","author":"Wakai","year":"2000","journal-title":"Acta Mater."},{"key":"mlstad3e4bbib50","doi-asserted-by":"publisher","DOI":"10.1016\/j.matdes.2022.111032","article-title":"A novel physics-regularized interpretable machine learning model for grain growth","volume":"222","author":"Yan","year":"2022","journal-title":"Mater. Design"},{"key":"mlstad3e4bbib51","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1016\/j.scriptamat.2017.11.023","article-title":"Comparison of coarsening behaviour in non-conserved and volume-conserved isotropic two-phase grain structures","volume":"146","author":"Yadav","year":"2018","journal-title":"Scr. Mater."},{"key":"mlstad3e4bbib52","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.92.063308","article-title":"Geometric and topological properties of the canonical grain-growth microstructure","volume":"92","author":"Mason","year":"2015","journal-title":"Phys. Rev. E"},{"key":"mlstad3e4bbib53","doi-asserted-by":"publisher","first-page":"8015","DOI":"10.1016\/j.jcp.2009.07.020","article-title":"Diffusion generated motion for grain growth in two and three dimensions","volume":"228","author":"Elsey","year":"2009","journal-title":"J. Comput. Phys."},{"key":"mlstad3e4bbib54","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1016\/0001-6160(65)90200-2","article-title":"On the theory of normal and abnormal grain growth","volume":"13","author":"Hillert","year":"1964","journal-title":"Acta Metall."},{"key":"mlstad3e4bbib55","doi-asserted-by":"publisher","first-page":"3986","DOI":"10.1016\/j.actamat.2013.03.013","article-title":"Schlegel description of grain form evolution in grain growth","volume":"61","author":"Patterson","year":"2013","journal-title":"Acta Mater."},{"key":"mlstad3e4bbib56","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1007\/BF02259844","article-title":"Short communication\/kurze mitteilung local error estimation by doubling","volume":"34","author":"Shampine","year":"1985","journal-title":"Computing"},{"key":"mlstad3e4bbib57","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1146\/annurev.matsci.32.112001.132041","article-title":"Phase-field models for microstructure evolution","volume":"32","author":"Chen","year":"2008","journal-title":"Annu. Rev. Mater. Res."},{"key":"mlstad3e4bbib58","doi-asserted-by":"publisher","DOI":"10.1088\/0965-0393\/17\/7\/073001","article-title":"Phase-field models in materials science","volume":"17","author":"Steinbach","year":"2009","journal-title":"Modelling Simul. Mater. Sci. Eng."},{"key":"mlstad3e4bbib59","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevB.78.024113","article-title":"Quantitative analysis of grain boundary properties in a generalized phase field model for grain growth in anisotropic systems","volume":"78","author":"Moelans","year":"2008","journal-title":"Phys. Rev. B"},{"key":"mlstad3e4bbib60","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1016\/0001-6160(72)90037-5","article-title":"Ground state structures in ordered binary alloys with second neighbor interactions","volume":"20","author":"Allen","year":"1972","journal-title":"Acta Metall."},{"key":"mlstad3e4bbib61","doi-asserted-by":"publisher","first-page":"1261","DOI":"10.1016\/0036-9748(73)90073-2","article-title":"A correction to the ground state of fcc binary ordered alloys with first and second neighbor pairwise interactions","volume":"7","author":"Allen","year":"1973","journal-title":"Scr. Metall."},{"key":"mlstad3e4bbib62","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1016\/B978-0-444-82537-7.X5000-1","article-title":"Voronoi diagrams","volume":"vol 5","author":"Aurenhammer","year":"2000"},{"key":"mlstad3e4bbib63","article-title":"Tensorflow: large-scale machine learning on heterogeneous distributed systems","author":"Abadi","year":"2016"},{"key":"mlstad3e4bbib64","article-title":"Layer normalization","author":"Ba","year":"2016"},{"key":"mlstad3e4bbib65","article-title":"Deep residual learning for image recognition","author":"He","year":"2015"},{"key":"mlstad3e4bbib66","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: from error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."}],"container-title":["Machine Learning: Science and Technology"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad3e4b","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad3e4b\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad3e4b","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad3e4b\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad3e4b\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad3e4b\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad3e4b\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad3e4b\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,3]],"date-time":"2024-05-03T08:39:52Z","timestamp":1714725592000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad3e4b"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,3]]},"references-count":66,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,5,3]]},"published-print":{"date-parts":[[2024,6,1]]}},"URL":"https:\/\/doi.org\/10.1088\/2632-2153\/ad3e4b","relation":{},"ISSN":["2632-2153"],"issn-type":[{"value":"2632-2153","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,3]]},"assertion":[{"value":"Accelerate microstructure evolution simulation using graph neural networks with adaptive spatiotemporal resolution","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 2024 The Author(s). Published by IOP Publishing Ltd","name":"copyright_information","label":"Copyright Information"},{"value":"2023-11-06","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2024-04-12","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2024-05-03","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}