{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T16:46:33Z","timestamp":1765039593693,"version":"3.37.3"},"reference-count":62,"publisher":"IOP Publishing","issue":"1","license":[{"start":{"date-parts":[[2022,2,11]],"date-time":"2022-02-11T00:00:00Z","timestamp":1644537600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,2,11]],"date-time":"2022-02-11T00:00:00Z","timestamp":1644537600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"name":"U.S. Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), Materials Sciences and Engineering Division"},{"DOI":"10.13039\/100006228","name":"Oak Ridge National Laboratory","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100006228","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Center for Nanophase Materials Sciences"},{"name":"U.S. Department of Energy, Office of Science"}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Mach. Learn.: Sci. Technol."],"published-print":{"date-parts":[[2022,3,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Scanning transmission electron microscopy is now the primary tool for exploring functional materials on the atomic level. Often, features of interest are highly localized in specific regions in the material, such as ferroelectric domain walls, extended defects, or second phase inclusions. Selecting regions to image for structural and chemical discovery via atomically resolved imaging has traditionally proceeded via human operators making semi-informed judgements on sampling locations and parameters. Recent efforts at automation for structural and physical discovery have pointed towards the use of \u2018active learning\u2019 methods that utilize Bayesian optimization with surrogate models to quickly find relevant regions of interest. Yet despite the potential importance of this direction, there is a general lack of certainty in selecting relevant control algorithms and how to balance <jats:italic>a priori<\/jats:italic> knowledge of the material system with knowledge derived during experimentation. Here we address this gap by developing the automated experiment workflows with several combinations to both illustrate the effects of these choices and demonstrate the tradeoffs associated with each in terms of accuracy, robustness, and susceptibility to hyperparameters for structural discovery. We discuss possible methods to build descriptors using the raw image data and deep learning based semantic segmentation, as well as the implementation of variational autoencoder based representation. Furthermore, each workflow is applied to a range of feature sizes including NiO pillars within a La:SrMnO<jats:sub>3<\/jats:sub> matrix, ferroelectric domains in BiFeO<jats:sub>3<\/jats:sub>, and topological defects in graphene. The code developed in this manuscript is open sourced and will be released at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/nccreang\/AE_Workflows\" xlink:type=\"simple\">github.com\/nccreang\/AE_Workflows<\/jats:ext-link>.<\/jats:p>","DOI":"10.1088\/2632-2153\/ac3844","type":"journal-article","created":{"date-parts":[[2021,11,10]],"date-time":"2021-11-10T22:31:23Z","timestamp":1636583483000},"page":"015024","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Towards automating structural discovery in scanning transmission electron microscopy\n                  <sup>*<\/sup>"],"prefix":"10.1088","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2764-7384","authenticated-orcid":true,"given":"Nicole","family":"Creange","sequence":"first","affiliation":[]},{"given":"Ondrej","family":"Dyck","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4692-8579","authenticated-orcid":true,"given":"Rama K","family":"Vasudevan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2570-4592","authenticated-orcid":true,"given":"Maxim","family":"Ziatdinov","sequence":"additional","affiliation":[]},{"given":"Sergei V","family":"Kalinin","sequence":"additional","affiliation":[]}],"member":"266","published-online":{"date-parts":[[2022,2,11]]},"reference":[{"key":"mlstac3844bib1","first-page":"p 327","volume":"vol 153","author":"Pennycook","year":"2008"},{"key":"mlstac3844bib2","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1093\/jmicro\/dfn030","article-title":"Atomic-resolution spectroscopic imaging: past, present and future","volume":"58","author":"Pennycook","year":"2009","journal-title":"J. Electron Microsc."},{"key":"mlstac3844bib3","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1038\/s41586-018-0298-5","article-title":"Electron ptychography of 2D materials to deep sub-angstrom resolution","volume":"559","author":"Jiang","year":"2018","journal-title":"Nature"},{"key":"mlstac3844bib4","doi-asserted-by":"publisher","first-page":"4155","DOI":"10.1038\/ncomms5155","article-title":"Picometre-precision analysis of scanning transmission electron microscopy images of platinum nanocatalysts","volume":"5","author":"Yankovich","year":"2014","journal-title":"Nat. Commun."},{"key":"mlstac3844bib5","doi-asserted-by":"publisher","first-page":"571","DOI":"10.1038\/nature08879","article-title":"Atom-by-atom structural and chemical analysis by annular dark-field electron microscopy","volume":"464","author":"Krivanek","year":"2010","journal-title":"Nature"},{"key":"mlstac3844bib6","doi-asserted-by":"publisher","first-page":"1420","DOI":"10.1126\/science.1200605","article-title":"Direct observation of continuous electric dipole rotation in flux-closure domains in ferroelectric Pb(Zr,Ti)O(3)","volume":"331","author":"Jia","year":"2011","journal-title":"Science"},{"key":"mlstac3844bib7","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.92.095502","article-title":"Spectroscopic imaging of single atoms within a bulk solid","volume":"92","author":"Varela","year":"2004","journal-title":"Phys. Rev. Lett."},{"key":"mlstac3844bib8","doi-asserted-by":"publisher","first-page":"3044","DOI":"10.1073\/pnas.0507105103","article-title":"Depth sectioning with the aberration-corrected scanning transmission electron microscope","volume":"103","author":"Borisevich","year":"2006","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"mlstac3844bib9","doi-asserted-by":"publisher","DOI":"10.1063\/1.4965709","article-title":"Single atom visibility in STEM optical depth sectioning","volume":"109","author":"Ishikawa","year":"2016","journal-title":"Appl. Phys. Lett."},{"key":"mlstac3844bib10","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.120.095901","article-title":"Temperature measurement by a nanoscale electron probe using energy gain and loss spectroscopy","volume":"120","author":"Idrobo","year":"2018","journal-title":"Phys. Rev. Lett."},{"key":"mlstac3844bib11","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.ultramic.2019.112890","article-title":"Patterned probes for high precision 4D-STEM bragg measurements","volume":"209","author":"Zeltmann","year":"2020","journal-title":"Ultramicroscopy"},{"key":"mlstac3844bib12","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1063\/5.0026121","article-title":"Disentangling nanoscale electric and magnetic fields by time-reversal operation in differential phase-contrast STEM","volume":"117","author":"Campanini","year":"2020","journal-title":"Appl. Phys. Lett."},{"key":"mlstac3844bib13","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1103\/PhysRevB.101.184116","article-title":"Atomic-resolution differential phase contrast STEM on ferroelectric materials: a mean-field approach","volume":"101","author":"Campanini","year":"2020","journal-title":"Phys. Rev. B"},{"key":"mlstac3844bib14","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1103\/PhysRevX.4.011013","article-title":"Generation of nondiffracting electron bessel beams","volume":"4","author":"Grillo","year":"2014","journal-title":"Phys. Rev. X"},{"key":"mlstac3844bib15","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1103\/PhysRevApplied.11.044072","article-title":"Electron-beam shaping in the transmission electron microscope: control of electron-beam propagation along atomic columns","volume":"11","author":"Rotunno","year":"2019","journal-title":"Phys. Rev. Appl."},{"key":"mlstac3844bib16","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.113.155501","article-title":"Direct observation of dopant atom diffusion in a bulk semiconductor crystal enhanced by a large size mismatch","volume":"113","author":"Ishikawa","year":"2014","journal-title":"Phys. Rev. Lett."},{"key":"mlstac3844bib17","doi-asserted-by":"publisher","first-page":"8908","DOI":"10.1002\/anie.201403382","article-title":"Direct observation of atomic dynamics and silicon doping at a topological defect in graphene","volume":"53","author":"Yang","year":"2014","journal-title":"Angew. Chem., Int. Ed."},{"key":"mlstac3844bib18","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nnano.2014.81","article-title":"Flexible metallic nanowires with self-adaptive contacts to semiconducting transition-metal dichalcogenide monolayers","volume":"9","author":"Lin","year":"2014","journal-title":"Nat. Nanotechnol."},{"key":"mlstac3844bib19","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/j.ultramic.2017.03.005","article-title":"Manipulating low-dimensional materials down to the level of single atoms with electron irradiation","volume":"180","author":"Susi","year":"2017","journal-title":"Ultramicroscopy"},{"key":"mlstac3844bib20","doi-asserted-by":"publisher","DOI":"10.1063\/1.4998599","article-title":"Placing single atoms in graphene with a scanning transmission electron microscope","volume":"111","author":"Dyck","year":"2017","journal-title":"Appl. Phys. Lett."},{"key":"mlstac3844bib21","doi-asserted-by":"publisher","DOI":"10.1002\/smll.201801771","article-title":"Building structures atom by atom via electron beam manipulation","volume":"14","author":"Dyck","year":"2018","journal-title":"Small"},{"key":"mlstac3844bib22","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6528\/aabb79","article-title":"Direct atomic fabrication and dopant positioning in Si using electron beams with active real-time image-based feedback","volume":"29","author":"Jesse","year":"2018","journal-title":"Nanotechnology"},{"key":"mlstac3844bib23","doi-asserted-by":"publisher","first-page":"9068","DOI":"10.1021\/acsnano.6b04212","article-title":"Big, deep, and smart data in scanning probe microscopy","volume":"10","author":"Kalinin","year":"2016","journal-title":"ACS Nano"},{"key":"mlstac3844bib24","doi-asserted-by":"publisher","first-page":"C4E39","DOI":"10.1116\/1.3374719","article-title":"Open source scanning probe microscopy control software package GXSM","volume":"28","author":"Zahl","year":"2010","journal-title":"J. Vac. Sci. Technol. B"},{"key":"mlstac3844bib25","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1038\/s42005-020-0317-3","article-title":"Artificial-intelligence-driven scanning probe microscopy","volume":"3","author":"Krull","year":"2020","journal-title":"Commun. Phys."},{"key":"mlstac3844bib26","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1038\/539485a","article-title":"Fire up the atom forge","volume":"539","author":"Kalinin","year":"2016","journal-title":"Nature"},{"key":"mlstac3844bib27","doi-asserted-by":"publisher","first-page":"669","DOI":"10.1557\/mrs.2019.211","article-title":"A self-driving microscope and the atomic forge","volume":"44","author":"Dyck","year":"2019","journal-title":"MRS Bull."},{"key":"mlstac3844bib28","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1103\/PhysRevLett.76.459","article-title":"Direct measurement of surface diffusion using atom-tracking scanning tunneling microscopy","volume":"76","author":"Swartzentruber","year":"1995","journal-title":"Phys. Rev. Lett."},{"key":"mlstac3844bib29","doi-asserted-by":"publisher","DOI":"10.1088\/0957-4484\/20\/25\/255701","article-title":"Adaptive probe trajectory scanning probe microscopy for multiresolution measurements of interface geometry","volume":"20","author":"Ovchinnikov","year":"2009","journal-title":"Nanotechnology"},{"author":"Requicha","key":"mlstac3844bib30","first-page":"81"},{"author":"Mokaberi","key":"mlstac3844bib31","first-page":"1406"},{"key":"mlstac3844bib32","doi-asserted-by":"publisher","first-page":"512","DOI":"10.1177\/0278364908100926","article-title":"Algorithms and software for nanomanipulation with atomic force microscopes","volume":"28","author":"Requicha","year":"2009","journal-title":"Int. J. Rob. Res."},{"key":"mlstac3844bib33","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1093\/jmicro\/dft042","article-title":"The potential for Bayesian compressive sensing to significantly reduce electron dose in high-resolution STEM images","volume":"63","author":"Stevens","year":"2014","journal-title":"Microscopy"},{"key":"mlstac3844bib34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40679-015-0009-3","article-title":"Applying compressive sensing to TEM video: a substantial frame rate increase on any camera","volume":"1","author":"Stevens","year":"2015","journal-title":"Adv. Struct. Chem. Imag."},{"key":"mlstac3844bib35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40679-016-0020-3","article-title":"Dynamic scan control in STEM: spiral scans","volume":"2","author":"Sang","year":"2016","journal-title":"Adv. Struct. Chem. Imag."},{"key":"mlstac3844bib36","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1017\/s143192761801543x","article-title":"Compressed sensing of scanning transmission electron microscopy (STEM) with nonrectangular scans","volume":"24","author":"Li","year":"2018","journal-title":"Microsc. Microanal."},{"key":"mlstac3844bib37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-65261-0","article-title":"Partial scanning transmission electron microscopy with deep learning","volume":"10","author":"Ede","year":"2020","journal-title":"Sci. Rep."},{"key":"mlstac3844bib38","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1002\/smll.202002878","article-title":"Fast scanning probe microscopy via machine learning: non-rectangular scans with compressed sensing and Gaussian process optimization","volume":"16","author":"Kelley","year":"2020","journal-title":"Small"},{"key":"mlstac3844bib39","doi-asserted-by":"publisher","first-page":"750","DOI":"10.1016\/j.carbon.2020.01.042","article-title":"Electron-beam introduction of heteroatomic Pt-Si structures in graphene","volume":"161","author":"Dyck","year":"2020","journal-title":"Carbon"},{"key":"mlstac3844bib40","doi-asserted-by":"publisher","first-page":"10855","DOI":"10.1021\/acsanm.0c02118","article-title":"Doping of Cr in graphene using electron beam manipulation for functional defect engineering","volume":"3","author":"Dyck","year":"2020","journal-title":"Appl. Nano Mater."},{"key":"mlstac3844bib41","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1016\/j.carbon.2020.11.015","article-title":"Doping transition-metal atoms in graphene for atomic-scale tailoring of electronic, magnetic, and quantum topological properties","volume":"173","author":"Dyck","year":"2021","journal-title":"Carbon"},{"key":"mlstac3844bib42","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.113.115501","article-title":"Silicon-carbon bond inversions driven by 60-keV electrons in graphene","volume":"113","author":"Susi","year":"2014","journal-title":"Phys. Rev. Lett."},{"key":"mlstac3844bib43","doi-asserted-by":"publisher","first-page":"5319","DOI":"10.1021\/acs.nanolett.8b02406","article-title":"Electron-beam manipiulation of silicon dopants in graphene","volume":"18","author":"Tripathi","year":"2018","journal-title":"Nano Lett."},{"key":"mlstac3844bib44","doi-asserted-by":"publisher","first-page":"2252","DOI":"10.1126\/sciadv.aav2252","article-title":"Engineering single-atom dynamics with electron irradiation","volume":"5","author":"Su","year":"2019","journal-title":"Sci. Adv."},{"key":"mlstac3844bib45","doi-asserted-by":"publisher","first-page":"13136","DOI":"10.1021\/acs.jpcc.9b01894","article-title":"Silicon substitution in nanotubes and graphene via intermittent vacancies","volume":"123","author":"Inani","year":"2019","journal-title":"J. Phys. Chem. C"},{"key":"mlstac3844bib46","doi-asserted-by":"publisher","DOI":"10.1002\/smll.202100693","article-title":"Atomically precise control of carbon insertion into hBN monolayer point vacancies using a focused electron beam guide","volume":"17","author":"Park","year":"2021","journal-title":"Small"},{"key":"mlstac3844bib47","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1017\/S143192761900134X","article-title":"Nion swift: open source image processing software for instrument control, data acquisition, organization, visualization, and analysis using python","volume":"25","author":"Meyer","year":"2019","journal-title":"Microsc. Microanal."},{"year":"2021","author":"","key":"mlstac3844bib48"},{"key":"mlstac3844bib49","doi-asserted-by":"publisher","first-page":"3217","DOI":"10.1039\/D0MH01324B","article-title":"Unusual electrical conductivity driven by localized stoichiometry modification at vertical epitaxial interfaces","volume":"7","author":"Zhang","year":"2020","journal-title":"Mater. Horizons"},{"article-title":"Deep bayesian local crystallography","year":"2020","author":"Kalinin","key":"mlstac3844bib50"},{"key":"mlstac3844bib51","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1038\/s41524-021-00569-7","article-title":"Ensemble learning-iterative training machine learning for uncertainty quantification and automated experiment in atom-resolved microscopy","volume":"7","author":"Ghosh","year":"2021","journal-title":"npj Comput. Mater."},{"key":"mlstac3844bib52","doi-asserted-by":"publisher","DOI":"10.1063\/5.0016792","article-title":"Predictability as a probe of manifest and latent physics: the case of atomic scale structural, chemical, and polarization behaviors in multiferroic Sm-doped BiFeO3","volume":"8","author":"Ziatdinov","year":"2021","journal-title":"Appl. Phys. Rev."},{"key":"mlstac3844bib53","doi-asserted-by":"publisher","first-page":"6361","DOI":"10.1038\/s41467-020-19907-2","article-title":"Exploring physics of ferroelectric domain walls via Bayesian analysis of atomically resolved STEM data","volume":"11","author":"Nelson","year":"2020","journal-title":"Nat. Commun."},{"article-title":"Zenodo, Nature Communications","year":"2020","author":"Nelson","key":"mlstac3844bib54"},{"article-title":"Ensemble learning and iterative training (ELIT) machine learning: applications towards uncertainty quantification and automated experiment in atom-resolved microscopy","year":"2021","author":"Ghosh","key":"mlstac3844bib55"},{"article-title":"Unsupervised machine learning discovery of chemical and physical transformation pathways from imaging data","year":"2021","author":"Kalinin","key":"mlstac3844bib56"},{"key":"mlstac3844bib57","doi-asserted-by":"publisher","first-page":"12742","DOI":"10.1021\/acsnano.7b07504","article-title":"Deep learning of atomically resolved scanning transmission electron microscopy images: chemical identification and tracking local transformations","volume":"11","author":"Ziatdinov","year":"2017","journal-title":"ACS Nano"},{"key":"mlstac3844bib58","doi-asserted-by":"publisher","first-page":"3000","DOI":"10.1017\/S1431927621010436","article-title":"AtomAI: open-source software for applications of deep learning to microscopy data","volume":"27","author":"Ziatdinov","year":"2021","journal-title":"Microsc. Microanal."},{"key":"mlstac3844bib59","doi-asserted-by":"publisher","DOI":"10.1063\/5.0012761","article-title":"Investigating phase transitions from local crystallographic analysis based on statistical learning of atomic environments in 2D MoS2-ReS2","volume":"8","author":"Vasudevan","year":"2021","journal-title":"Appl. Phys. Rev."},{"key":"mlstac3844bib60","doi-asserted-by":"publisher","DOI":"10.1063\/1.4914016","article-title":"Big data in reciprocal space: sliding fast Fourier transforms for determining periodicity","volume":"106","author":"Vasudevan","year":"2015","journal-title":"Appl. Phys. Lett."},{"year":"2005","author":"Rasmussen","key":"mlstac3844bib61"},{"first-page":"266","author":"Winter","key":"mlstac3844bib62","doi-asserted-by":"publisher","DOI":"10.1117\/12.366289"}],"container-title":["Machine Learning: Science and Technology"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ac3844","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ac3844\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ac3844","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ac3844\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ac3844\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ac3844\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ac3844\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ac3844\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,2,11]],"date-time":"2022-02-11T15:37:46Z","timestamp":1644593866000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ac3844"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,11]]},"references-count":62,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,2,11]]},"published-print":{"date-parts":[[2022,3,1]]}},"URL":"https:\/\/doi.org\/10.1088\/2632-2153\/ac3844","relation":{},"ISSN":["2632-2153"],"issn-type":[{"type":"electronic","value":"2632-2153"}],"subject":[],"published":{"date-parts":[[2022,2,11]]},"assertion":[{"value":"Towards automating structural discovery in scanning transmission electron microscopy\n                  *","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 2022 The Author(s). Published by IOP Publishing Ltd","name":"copyright_information","label":"Copyright Information"},{"value":"2021-07-28","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2021-11-10","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2022-02-11","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}