{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T08:57:50Z","timestamp":1777453070022,"version":"3.51.4"},"reference-count":63,"publisher":"IOP Publishing","issue":"4","license":[{"start":{"date-parts":[[2024,11,11]],"date-time":"2024-11-11T00:00:00Z","timestamp":1731283200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2024,11,11]],"date-time":"2024-11-11T00:00:00Z","timestamp":1731283200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100006151","name":"Basic Energy Sciences","doi-asserted-by":"crossref","award":["FWPPS-030"],"award-info":[{"award-number":["FWPPS-030"]}],"id":[{"id":"10.13039\/100006151","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100006231","name":"Brookhaven National Laboratory","doi-asserted-by":"crossref","award":["DE-SC0012704"],"award-info":[{"award-number":["DE-SC0012704"]}],"id":[{"id":"10.13039\/100006231","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100006227","name":"Lawrence Livermore National Laboratory","doi-asserted-by":"crossref","award":["22-ERD-014"],"award-info":[{"award-number":["22-ERD-014"]}],"id":[{"id":"10.13039\/100006227","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100007161","name":"Boston University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100007161","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":[[2024,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Spectroscopy techniques such as x-ray absorption near edge structure (XANES) provide valuable insights into the atomic structures of materials, yet the inverse prediction of precise structures from spectroscopic data remains a formidable challenge. In this study, we introduce a framework that combines generative artificial intelligence models with XANES spectroscopy to predict three-dimensional atomic structures of disordered systems, using amorphous carbon (<jats:italic>a<\/jats:italic>-C) as a model system. In this work, we introduce a new framework based on the diffusion model, a recent generative machine learning method, to predict 3D structures of disordered materials from a target property. For demonstration, we apply the model to identify the atomic structures of <jats:italic>a<\/jats:italic>-C as a representative material system from the target XANES spectra. We show that conditional generation guided by XANES spectra reproduces key features of the target structures. Furthermore, we show that our model can steer the generative process to tailor atomic arrangements for a specific XANES spectrum. Finally, our generative model exhibits a remarkable scale-agnostic property, thereby enabling generation of realistic, large-scale structures through learning from a small-scale dataset (i.e. with small unit cells). Our work represents a significant stride in bridging the gap between materials characterization and atomic structure determination; in addition, it can be leveraged for materials discovery in exploring various material properties as targeted.<\/jats:p>","DOI":"10.1088\/2632-2153\/ad8c10","type":"journal-article","created":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T22:57:27Z","timestamp":1730156247000},"page":"045037","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Spectroscopy-guided discovery of three-dimensional structures of disordered materials with diffusion models"],"prefix":"10.1088","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4828-8598","authenticated-orcid":true,"given":"Hyuna","family":"Kwon","sequence":"first","affiliation":[]},{"given":"Tim","family":"Hsu","sequence":"additional","affiliation":[]},{"given":"Wenyu","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Wonseok","family":"Jeong","sequence":"additional","affiliation":[]},{"given":"Fikret","family":"Aydin","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8451-0275","authenticated-orcid":true,"given":"James","family":"Chapman","sequence":"additional","affiliation":[]},{"given":"Xiao","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Vincenzo","family":"Lordi","sequence":"additional","affiliation":[]},{"given":"Matthew R","family":"Carbone","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4351-6085","authenticated-orcid":true,"given":"Deyu","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Fei","family":"Zhou","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0025-7263","authenticated-orcid":true,"given":"Tuan","family":"Anh Pham","sequence":"additional","affiliation":[]}],"member":"266","published-online":{"date-parts":[[2024,11,11]]},"reference":[{"key":"mlstad8c10bib1","doi-asserted-by":"publisher","first-page":"845","DOI":"10.1038\/nmat2297","article-title":"Materials for electrochemical capacitors","volume":"7","author":"Simon","year":"2008","journal-title":"Nat. Mater."},{"key":"mlstad8c10bib2","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1016\/j.mattod.2014.10.040","article-title":"Li-ion battery materials: present and future","volume":"18","author":"Nitta","year":"2015","journal-title":"Mater. Today"},{"key":"mlstad8c10bib3","doi-asserted-by":"publisher","first-page":"2737","DOI":"10.1021\/acs.chemrev.2c00155","article-title":"Fluids and electrolytes under confinement in single-digit nanopores","volume":"123","author":"Aluru","year":"2023","journal-title":"Chem. Rev."},{"key":"mlstad8c10bib4","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1016\/j.ccr.2004.02.014","article-title":"Progress in the theory and interpretation of xanes","volume":"249","author":"Rehr","year":"2005","journal-title":"Coord. Chem. Rev."},{"key":"mlstad8c10bib5","doi-asserted-by":"publisher","first-page":"33","DOI":"10.2138\/rmg.2014.78.2","article-title":"Fundamentals of XAFS","volume":"78","author":"Newville","year":"2014","journal-title":"Rev. Mineral. Geochem."},{"key":"mlstad8c10bib6","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/S0360-0564(08)60602-1","article-title":"The use of x-ray K-absorption edges in the study of catalytically active solids","volume":"12","author":"Van Nordsthand","year":"1960","journal-title":"Adv. Catal."},{"key":"mlstad8c10bib7","doi-asserted-by":"publisher","first-page":"4924","DOI":"10.1021\/acs.jpclett.2c00624","article-title":"Deep reinforcement learning for molecular inverse problem of nuclear magnetic resonance spectra to molecular structure","volume":"13","author":"Sridharan","year":"2022","journal-title":"J. Phys. Chem. Lett."},{"key":"mlstad8c10bib8","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevMaterials.3.033604","article-title":"Classification of local chemical environments from x-ray absorption spectra using supervised machine learning","volume":"3","author":"Carbone","year":"2019","journal-title":"Phys. Rev. Mater."},{"key":"mlstad8c10bib9","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41524-021-00664-9","article-title":"Understanding x-ray absorption spectra by means of descriptors and machine learning algorithms","volume":"7","author":"Guda","year":"2021","journal-title":"npj Comput. Mater."},{"key":"mlstad8c10bib10","doi-asserted-by":"publisher","first-page":"1370","DOI":"10.1016\/j.matt.2019.08.017","article-title":"Inverse design of solid-state materials via a continuous representation","volume":"1","author":"Noh","year":"2019","journal-title":"Matter"},{"key":"mlstad8c10bib11","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":"mlstad8c10bib12","doi-asserted-by":"publisher","first-page":"eaax9324","DOI":"10.1126\/sciadv.aax9324","article-title":"Inverse design of porous materials using artificial neural networks","volume":"6","author":"Kim","year":"2020","journal-title":"Sci. Adv."},{"key":"mlstad8c10bib13","doi-asserted-by":"publisher","first-page":"4871","DOI":"10.1039\/D0SC00594K","article-title":"Machine-enabled inverse design of inorganic solid materials: promises and challenges","volume":"11","author":"Noh","year":"2020","journal-title":"Chem. Sci."},{"key":"mlstad8c10bib14","first-page":"pp 2256","article-title":"Deep unsupervised learning using nonequilibrium thermodynamics","author":"Sohl-Dickstein","year":"2015"},{"key":"mlstad8c10bib15","first-page":"pp 6840","article-title":"Denoising diffusion probabilistic models","volume":"vol 33","author":"Ho","year":"2020"},{"key":"mlstad8c10bib16","article-title":"Score-based generative modeling through stochastic differential equations","author":"Song","year":"2020"},{"key":"mlstad8c10bib17","first-page":"695","article-title":"Estimation of non-normalized statistical models by score matching","volume":"6","author":"Hyv\u00e4rinen","year":"2005","journal-title":"J. Mach. Learn. Res."},{"key":"mlstad8c10bib18","doi-asserted-by":"publisher","first-page":"1661","DOI":"10.1162\/NECO_a_00142","article-title":"A connection between score matching and denoising autoencoders","volume":"23","author":"Vincent","year":"2011","journal-title":"Neural Comput."},{"key":"mlstad8c10bib19","first-page":"pp 8867","article-title":"Equivariant diffusion for molecule generation in 3d","author":"Hoogeboom","year":"2022"},{"key":"mlstad8c10bib20","first-page":"pp 24240","article-title":"Torsional diffusion for molecular conformer generation","volume":"vol 35","author":"Jing","year":"2022"},{"key":"mlstad8c10bib21","article-title":"Digress: discrete denoising diffusion for graph generation","author":"Vignac","year":"2022"},{"key":"mlstad8c10bib22","article-title":"Geodiff: a geometric diffusion model for molecular conformation generation","author":"Xu","year":"2022"},{"key":"mlstad8c10bib23","doi-asserted-by":"publisher","first-page":"873","DOI":"10.1038\/s43588-023-00532-0","article-title":"Guided diffusion for inverse molecular design","volume":"3","author":"Weiss","year":"2023","journal-title":"Nat. Comput. Sci."},{"key":"mlstad8c10bib24","article-title":"Crystal diffusion variational autoencoder for periodic material generation","author":"Xie","year":"2021"},{"key":"mlstad8c10bib25","article-title":"Towards predicting equilibrium distributions for molecular systems with deep learning","author":"Zheng","year":"2023"},{"key":"mlstad8c10bib26","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1038\/nmat4803","article-title":"Modelling heterogeneous interfaces for solar water splitting","volume":"16","author":"Pham","year":"2017","journal-title":"Nat. Mater."},{"key":"mlstad8c10bib27","doi-asserted-by":"publisher","first-page":"5786","DOI":"10.1021\/acs.iecr.9b06617","article-title":"Beyond idealized models of nanoscale metal hydrides for hydrogen storage","volume":"59","author":"Wood","year":"2020","journal-title":"Ind. Eng. Chem. Res."},{"key":"mlstad8c10bib28","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1038\/s41524-021-00526-4","article-title":"Constrained crystals deep convolutional generative adversarial network for the inverse design of crystal structures","volume":"7","author":"Long","year":"2021","journal-title":"npj Comput. Mater."},{"key":"mlstad8c10bib29","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1038\/s41524-021-00670-x","article-title":"Inverse design of two-dimensional materials with invertible neural networks","volume":"7","author":"Fung","year":"2021","journal-title":"npj Comput. Mater."},{"key":"mlstad8c10bib30","doi-asserted-by":"publisher","DOI":"10.1088\/2632-2153\/aca1f7","article-title":"Atomic structure generation from reconstructing structural fingerprints","volume":"3","author":"Fung","year":"2022","journal-title":"Mach. Learn.: Sci. Technol."},{"key":"mlstad8c10bib31","article-title":"2D graph-based generative approach for exploring transition states using diffusion model","author":"Kim","year":"2023"},{"key":"mlstad8c10bib32","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevB.100.094107","article-title":"Deep-learning approach to the structure of amorphous silicon","volume":"100","author":"Comin","year":"2019","journal-title":"Phys. Rev. B"},{"key":"mlstad8c10bib33","doi-asserted-by":"publisher","first-page":"8532","DOI":"10.1021\/acs.jpclett.0c02535","article-title":"Generating multiscale amorphous molecular structures using deep learning: a study in 2D","volume":"11","author":"Kilgour","year":"2020","journal-title":"J. Phys. Chem. Lett."},{"key":"mlstad8c10bib34","article-title":"Wasserstein auto-encoders","author":"Tolstikhin","year":"2017"},{"key":"mlstad8c10bib35","doi-asserted-by":"publisher","first-page":"8670","DOI":"10.1039\/D2TA07075H","article-title":"Hydrogen in disordered titania: connecting local chemistry, structure and stoichiometry through accelerated exploration","volume":"11","author":"Chapman","year":"2023","journal-title":"J. Mater. Chem. A"},{"key":"mlstad8c10bib36","doi-asserted-by":"publisher","first-page":"17818","DOI":"10.1021\/ja407374k","article-title":"Molecular dynamics simulations of gas selectivity in amorphous porous molecular solids","volume":"135","author":"Jiang","year":"2013","journal-title":"J. Am. Chem. Soc."},{"key":"mlstad8c10bib37","doi-asserted-by":"publisher","first-page":"5915","DOI":"10.1021\/acsanm.2c01280","article-title":"Molecular dynamics modeling of interfacial interactions between flattened carbon nanotubes and amorphous carbon: implications for ultra-lightweight composites","volume":"5","author":"Gaikwad","year":"2022","journal-title":"ACS Appl. Nano Mater."},{"key":"mlstad8c10bib38","doi-asserted-by":"publisher","first-page":"2322","DOI":"10.1021\/acs.jctc.7b01296","article-title":"Reaxff molecular dynamics simulation for the graphitization of amorphous carbon: a parametric study","volume":"14","author":"Li","year":"2018","journal-title":"J. Chem. Theory Comput."},{"key":"mlstad8c10bib39","doi-asserted-by":"publisher","first-page":"784","DOI":"10.1103\/PhysRevB.28.784","article-title":"Bond-orientational order in liquids and glasses","volume":"28","author":"Steinhardt","year":"1983","journal-title":"Phys. Rev. B"},{"key":"mlstad8c10bib40","article-title":"Gap interatomic potential for amorphous carbon (2.0) [data set]","author":"Caro"},{"key":"mlstad8c10bib41","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevB.95.094203","article-title":"Machine learning based interatomic potential for amorphous carbon","volume":"95","author":"Deringer","year":"2017","journal-title":"Phys. Rev. B"},{"key":"mlstad8c10bib42","doi-asserted-by":"publisher","first-page":"16473","DOI":"10.1021\/acs.jpcc.3c02029","article-title":"Harnessing neural networks for elucidating x-ray absorption structure\u2013spectrum relationships in amorphous carbon","volume":"127","author":"Kwon","year":"2023","journal-title":"J. Phys. Chem. C"},{"key":"mlstad8c10bib43","doi-asserted-by":"publisher","DOI":"10.1088\/1361-651X\/ab45da","article-title":"Structural and elastic properties of amorphous carbon from simulated quenching at low rates","volume":"27","author":"Jana","year":"2019","journal-title":"Modelling Simul. Mater. Sci. Eng."},{"key":"mlstad8c10bib44","doi-asserted-by":"publisher","first-page":"370","DOI":"10.1016\/j.cam.2015.06.002","article-title":"The truncated euler\u2013maruyama method for stochastic differential equations","volume":"290","author":"Mao","year":"2015","journal-title":"J. Comput. Appl. Math."},{"key":"mlstad8c10bib45","doi-asserted-by":"publisher","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":"mlstad8c10bib46","doi-asserted-by":"publisher","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":"mlstad8c10bib47","article-title":"Unified representation of molecules and crystals for machine learning","author":"Huo","year":"2017"},{"key":"mlstad8c10bib48","article-title":"Diffusion posterior sampling for general noisy inverse problems","author":"Chung","year":"2022"},{"key":"mlstad8c10bib49","first-page":"pp 8026","article-title":"Pytorch: an imperative style, high-performance deep learning library","volume":"vol 32","author":"Paszke","year":"2019"},{"key":"mlstad8c10bib50","article-title":"Fast graph representation learning with pytorch geometric","author":"Fey","year":"2019"},{"key":"mlstad8c10bib51","first-page":"pp 7537","article-title":"Fourier features let networks learn high frequency functions in low dimensional domains","volume":"vol 33","author":"Tancik","year":"2020"},{"key":"mlstad8c10bib52","article-title":"Learning mesh-based simulation with graph networks","author":"Pfaff","year":"2020"},{"key":"mlstad8c10bib53","article-title":"Restart sampling for improving generative processes","author":"Xu","year":"2023"},{"key":"mlstad8c10bib54","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.96.215502","article-title":"X-ray absorption spectra of water from first principles calculations","volume":"96","author":"Prendergast","year":"2006","journal-title":"Phys. Rev. Lett."},{"key":"mlstad8c10bib55","doi-asserted-by":"publisher","DOI":"10.1088\/0953-8984\/21\/39\/395502","article-title":"Quantum espresso: a modular and open-source software project for quantum simulations of materials","volume":"21","author":"Giannozzi","year":"2009","journal-title":"J. Phys.: Condens. Matter"},{"key":"mlstad8c10bib56","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevB.80.235126","article-title":"Bloch-state-based interpolation: an efficient generalization of the shirley approach to interpolating electronic structure","volume":"80","author":"Prendergast","year":"2009","journal-title":"Phys. Rev. B"},{"key":"mlstad8c10bib57","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":"mlstad8c10bib58","doi-asserted-by":"publisher","first-page":"7892","DOI":"10.1103\/PhysRevB.41.7892","article-title":"Soft self-consistent pseudopotentials in a generalized eigenvalue formalism","volume":"41","author":"Vanderbilt","year":"1990","journal-title":"Phys. Rev. B"},{"key":"mlstad8c10bib59","doi-asserted-by":"publisher","first-page":"973","DOI":"10.1021\/acs.jpcc.0c08597","article-title":"Trends in carbon, oxygen and nitrogen core in the x-ray absorption spectroscopy of carbon nanomaterials: a guide for the perplexed","volume":"125","author":"Sainio","year":"2020","journal-title":"J. Phys. Chem. C"},{"key":"mlstad8c10bib60","doi-asserted-by":"publisher","first-page":"794","DOI":"10.1103\/PhysRevLett.80.794","article-title":"Ab initio inclusion of electron-hole attraction: application to x-ray absorption and resonant inelastic x-ray scattering","volume":"80","author":"Shirley","year":"1998","journal-title":"Phys. Rev. Lett."},{"key":"mlstad8c10bib61","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevB.85.045101","article-title":"Theoretical optical and x-ray spectra of liquid and solid H2o","volume":"85","author":"Vinson","year":"2012","journal-title":"Phys. Rev. B"},{"key":"mlstad8c10bib62","doi-asserted-by":"publisher","first-page":"3284","DOI":"10.1021\/ct3005613","article-title":"Linear-response and real-time time-dependent density functional theory studies of core-level near-edge x-ray absorption","volume":"8","author":"Lopata","year":"2012","journal-title":"J. Chem. Theory Comput."},{"key":"mlstad8c10bib63","doi-asserted-by":"publisher","first-page":"4144","DOI":"10.1021\/acs.chemmater.3c02957","article-title":"A Integrating machine learning potential and x-ray absorption spectroscopy for predicting the chemical speciation of disordered carbon nitrides","volume":"36","author":"Jeong","year":"2024","journal-title":"Chem. Mater."}],"container-title":["Machine Learning: Science and Technology"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad8c10","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad8c10\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad8c10","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad8c10\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad8c10\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad8c10\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad8c10\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad8c10\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,11]],"date-time":"2024-11-11T09:27:55Z","timestamp":1731317275000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad8c10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,11]]},"references-count":63,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,11,11]]},"published-print":{"date-parts":[[2024,12,1]]}},"URL":"https:\/\/doi.org\/10.1088\/2632-2153\/ad8c10","relation":{},"ISSN":["2632-2153"],"issn-type":[{"value":"2632-2153","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,11]]},"assertion":[{"value":"Spectroscopy-guided discovery of three-dimensional structures of disordered materials with diffusion models","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":"2024-06-14","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2024-10-28","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2024-11-11","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}