{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T04:52:01Z","timestamp":1778647921293,"version":"3.51.4"},"reference-count":79,"publisher":"IOP Publishing","issue":"3","license":[{"start":{"date-parts":[[2024,8,13]],"date-time":"2024-08-13T00:00:00Z","timestamp":1723507200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2024,8,13]],"date-time":"2024-08-13T00:00:00Z","timestamp":1723507200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"name":"Danmarks Grundforskningsfond","award":["P3"],"award-info":[{"award-number":["P3"]}]},{"name":"Danmarks Frie Forskningsfond","award":["3164- 00297B"],"award-info":[{"award-number":["3164- 00297B"]}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Mach. Learn.: Sci. Technol."],"published-print":{"date-parts":[[2024,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Finding low-energy atomic ordering in compositionally complex materials is one of the hardest problems in materials discovery, the solution of which can lead to breakthroughs in functional materials\u2014from alloys to ceramics. In this work, we present the\n                    <jats:bold>Arti<\/jats:bold>\n                    ficial\n                    <jats:bold>S<\/jats:bold>\n                    tructure\n                    <jats:bold>A<\/jats:bold>\n                    rranging\n                    <jats:bold>N<\/jats:bold>\n                    et (\n                    <jats:bold>ArtiSAN<\/jats:bold>\n                    )\u2014a reinforcement learning agent utilizing graph representation that is trained to find low-energy atomic configurations of multicomponent systems through a series of atomic switch operations. ArtiSAN is trained on small alloy supercells ranging from binary to septenary. Strikingly, ArtiSAN generalizes to much larger systems of more than a thousand atoms, which are inaccessible with state-of-the-art methods due to the combinatorially larger search space. The performance of the current ArtiSAN agent is tested and deployed on several compositions that can be correlated with known experimental and high-fidelity computational structures. ArtiSAN demonstrates transfer across size and composition and finds physically meaningful structures using no energy evaluation calls once fully trained. While ArtiSAN will require further modifications to capture all variability in structure search, it is a remarkable step towards solving the structural part of the problem of disordered materials discovery.\n                  <\/jats:p>","DOI":"10.1088\/2632-2153\/ad69ff","type":"journal-article","created":{"date-parts":[[2024,7,31]],"date-time":"2024-07-31T20:47:57Z","timestamp":1722458877000},"page":"035043","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["ArtiSAN: navigating the complexity of material structures with deep reinforcement learning"],"prefix":"10.1088","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1283-6492","authenticated-orcid":true,"given":"Jonas","family":"Elsborg","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3198-5116","authenticated-orcid":true,"given":"Arghya","family":"Bhowmik","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2024,8,13]]},"reference":[{"key":"mlstad69ffbib1","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1080\/21663831.2014.912690","article-title":"High-entropy alloys: a critical review","volume":"2","author":"Tsai","year":"2014","journal-title":"Mater. Res. Lett."},{"key":"mlstad69ffbib2","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1016\/j.msea.2003.10.257","article-title":"Microstructural development in equiatomic multicomponent alloys","volume":"375","author":"Cantor","year":"2004","journal-title":"Mater. Sci. Eng. A"},{"key":"mlstad69ffbib3","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1002\/adem.200300567","article-title":"Nanostructured high-entropy alloys with multiple principal elements: novel alloy design concepts and outcomes","volume":"6","author":"Yeh","year":"2004","journal-title":"Adv. Eng. Mater."},{"key":"mlstad69ffbib4","doi-asserted-by":"publisher","first-page":"515","DOI":"10.1038\/s41578-019-0121-4","article-title":"High-entropy alloys","volume":"4","author":"George","year":"2019","journal-title":"Nat. Rev. Mater."},{"key":"mlstad69ffbib5","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1038\/s41578-019-0170-8","article-title":"High-entropy ceramics","volume":"5","author":"Oses","year":"2020","journal-title":"Nat. Rev. Mater."},{"key":"mlstad69ffbib6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.pmatsci.2013.10.001","article-title":"Microstructures and properties of high-entropy alloys","volume":"61","author":"Zhang","year":"2014","journal-title":"Prog. Mater. Sci."},{"key":"mlstad69ffbib7","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1126\/science.1254581","article-title":"A fracture-resistant high-entropy alloy for cryogenic applications","volume":"345","author":"Gludovatz","year":"2014","journal-title":"Science"},{"key":"mlstad69ffbib8","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1038\/s41586-022-05115-z","article-title":"Compositionally complex doping for zero-strain zero-cobalt layered cathodes","volume":"610","author":"Zhang","year":"2022","journal-title":"Nature"},{"key":"mlstad69ffbib9","doi-asserted-by":"publisher","first-page":"712","DOI":"10.1038\/s41586-021-03428-z","article-title":"Direct observation of chemical short-range order in a medium-entropy alloy","volume":"592","author":"Chen","year":"2021","journal-title":"Nature"},{"key":"mlstad69ffbib10","doi-asserted-by":"publisher","first-page":"464","DOI":"10.1016\/j.matchar.2018.06.019","article-title":"Ab initio phase stabilities and mechanical properties of multicomponent alloys: a comprehensive review for high entropy alloys and compositionally complex alloys","volume":"147","author":"Ikeda","year":"2019","journal-title":"Mater. Charact."},{"key":"mlstad69ffbib11","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevB.91.224204","article-title":"Atomic short-range order and incipient long-range order in high-entropy alloys","volume":"91","author":"Singh","year":"2015","journal-title":"Phys. Rev. B"},{"key":"mlstad69ffbib12","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.actamat.2020.02.041","article-title":"Chemical short range order strengthening in a model fcc high entropy alloy","volume":"190","author":"Antillon","year":"2020","journal-title":"Acta Mater."},{"key":"mlstad69ffbib13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41524-022-00779-7","article-title":"Composition design of high-entropy alloys with deep sets learning","volume":"8","author":"Zhang","year":"2022","journal-title":"npj Comput. Mater."},{"key":"mlstad69ffbib14","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.actamat.2019.03.010","article-title":"Machine learning assisted design of high entropy alloys with desired property","volume":"170","author":"Wen","year":"2019","journal-title":"Acta Mater."},{"key":"mlstad69ffbib15","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1016\/j.actamat.2019.03.012","article-title":"Machine-learning phase prediction of high-entropy alloys","volume":"169","author":"Huang","year":"2019","journal-title":"Acta Mater."},{"key":"mlstad69ffbib16","doi-asserted-by":"publisher","first-page":"736","DOI":"10.1038\/s41563-021-01013-3","article-title":"Electronic-structure methods for materials design","volume":"20","author":"Marzari","year":"2021","journal-title":"Nat. Mater."},{"key":"mlstad69ffbib17","first-page":"p 181","article-title":"Structure and stability prediction of compounds with evolutionary algorithms","author":"Revard","year":"2014"},{"key":"mlstad69ffbib18","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevB.78.064102","article-title":"Identifying the minimum-energy atomic configuration on a lattice: Lamarckian twist on Darwinian evolution","volume":"78","author":"d\u2019Avezac","year":"2008","journal-title":"Phys. Rev. B"},{"key":"mlstad69ffbib19","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevB.73.224208","article-title":"Structure and magnetism in bcc-based iron-cobalt alloys","volume":"73","author":"D\u00edaz-Ortiz","year":"2006","journal-title":"Phys. Rev. B"},{"key":"mlstad69ffbib20","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1007\/s11244-013-0160-9","article-title":"Genetic algorithm procreation operators for alloy nanoparticle catalysts","volume":"57","author":"Lysgaard","year":"2014","journal-title":"Top. Catal."},{"key":"mlstad69ffbib21","article-title":"Mastering chess and shogi by self-play with a general reinforcement learning algorithm","author":"Silver","year":"2017"},{"key":"mlstad69ffbib22","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1038\/s41586-021-04301-9","article-title":"Magnetic control of tokamak plasmas through deep reinforcement learning","volume":"602","author":"Degrave","year":"2022","journal-title":"Nature"},{"key":"mlstad69ffbib23","article-title":"Chip placement with deep reinforcement learning","author":"Mirhoseini","year":"2020"},{"key":"mlstad69ffbib24","first-page":"pp 8959","article-title":"Reinforcement learning for molecular design guided by quantum mechanics","author":"Simm","year":"2020"},{"key":"mlstad69ffbib25","article-title":"Scalable fragment-based 3D molecular design with reinforcement learning","author":"Flam-Shepherd","year":"2022"},{"key":"mlstad69ffbib26","doi-asserted-by":"publisher","first-page":"3731","DOI":"10.1021\/acs.jcim.3c00394","article-title":"Equivariant graph-representation-based actor\u2013critic reinforcement learning for nanoparticle design","volume":"63","author":"Elsborg","year":"2023","journal-title":"J. Chem. Inf. Model."},{"key":"mlstad69ffbib27","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1080\/14786445008521796","article-title":"XXII. Programming a computer for playing chess","volume":"41","author":"Shannon","year":"1950","journal-title":"London, Edinburgh Dublin Phil. Mag. J. Sci."},{"key":"mlstad69ffbib28","article-title":"The symmetry-adapted configurational ensemble approach to the computer simulation of site-disordered solids","author":"Grau-Crespo","year":"2016"},{"key":"mlstad69ffbib29","article-title":"Structure-aware generation of drug-like molecules","author":"Drot\u00e1r","year":"2021"},{"key":"mlstad69ffbib30","article-title":"Proximal policy optimization algorithms","author":"Schulman","year":"2017"},{"key":"mlstad69ffbib31","article-title":"MACE: higher order equivariant message passing neural networks for fast and accurate force fields","volume":"vol 35","author":"Batatia","year":"2022"},{"key":"mlstad69ffbib32","article-title":"High-dimensional continuous control using generalized advantage estimation","author":"Schulman","year":"2015"},{"key":"mlstad69ffbib33","doi-asserted-by":"publisher","DOI":"10.1039\/C7CP04641C","article-title":"Understanding the structure and reactivity of NiCu nanoparticles: an atomistic model","volume":"19","author":"Quaino","year":"2017","journal-title":"Phys. Chem. Chem. Phys."},{"key":"mlstad69ffbib34","doi-asserted-by":"publisher","DOI":"10.1039\/C5CP00215J","article-title":"Study of structures and thermodynamics of CuNi nanoalloys using a new DFT-fitted atomistic potential","volume":"17","author":"Panizon","year":"2015","journal-title":"Phys. Chem. Chem. Phys."},{"key":"mlstad69ffbib35","doi-asserted-by":"publisher","DOI":"10.1063\/1.4812323","article-title":"Commentary: The materials project: A materials genome approach to accelerating materials innovation","volume":"1","author":"Jain","year":"2013","journal-title":"APL Mater."},{"key":"mlstad69ffbib36","doi-asserted-by":"publisher","first-page":"1193","DOI":"10.1021\/acsanm.2c04767","article-title":"Ag@Pt core\u2013shell nanoparticles for plasmonic catalysis","volume":"6","author":"Fan","year":"2023","journal-title":"ACS Appl. Nano Mater."},{"key":"mlstad69ffbib37","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1039\/D2FD00116K","article-title":"Melting properties of AgxPt 1\u2212x nanoparticles","volume":"242","author":"Front","year":"2023","journal-title":"Faraday Discuss."},{"key":"mlstad69ffbib38","doi-asserted-by":"publisher","DOI":"10.1016\/j.jallcom.2023.171080","article-title":"Silver-platinum nanoparticles and nanodroplets supported on silica surfaces: structure and chemical ordering","volume":"961","author":"Hellal","year":"2023","journal-title":"J. Alloys Compd."},{"key":"mlstad69ffbib39","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1016\/j.colsurfa.2013.09.008","article-title":"Synthesis of core\u2013shell silver\u2013platinum nanoparticles, improving shell integrity","volume":"441","author":"Wojtysiak","year":"2014","journal-title":"Colloids Surf. A"},{"key":"mlstad69ffbib40","doi-asserted-by":"publisher","first-page":"135","DOI":"10.3390\/met7040135","article-title":"Face centred cubic multi-component equiatomic solid solutions in the Au-Cu-Ni-Pd-Pt system","volume":"7","author":"Freudenberger","year":"2017","journal-title":"Metals"},{"key":"mlstad69ffbib41","doi-asserted-by":"publisher","DOI":"10.1088\/0953-8984\/26\/3\/035402","article-title":"An adaptive genetic algorithm for crystal structure prediction","volume":"26","author":"Wu","year":"2013","journal-title":"J. Phys.: Condens. Matter"},{"key":"mlstad69ffbib42","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.112.045502","article-title":"Exploring the structural complexity of intermetallic compounds by an adaptive genetic algorithm","volume":"112","author":"Zhao","year":"2014","journal-title":"Phys. Rev. Lett."},{"key":"mlstad69ffbib43","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1007\/s12540-013-3020-z","article-title":"Prediction of atomic structure of Pt-based bimetallic nanoalloys by using genetic algorithm","volume":"19","author":"Oh","year":"2013","journal-title":"Met. Mater. Int."},{"key":"mlstad69ffbib44","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1080\/10426914.2011.552021","article-title":"Predicting the structure of alloys using genetic algorithms","volume":"26","author":"Mohn","year":"2011","journal-title":"Mater. Manuf. Process."},{"key":"mlstad69ffbib45","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1038\/s41586-021-03544-w","article-title":"A graph placement methodology for fast chip design","volume":"594","author":"Mirhoseini","year":"2021","journal-title":"Nature"},{"key":"mlstad69ffbib46","doi-asserted-by":"publisher","first-page":"1609","DOI":"10.1016\/j.compstruc.2004.05.005","article-title":"Graph representation for structural topology optimization using genetic algorithms","volume":"82","author":"Wang","year":"2004","journal-title":"Comput. Struct."},{"key":"mlstad69ffbib47","doi-asserted-by":"publisher","first-page":"771","DOI":"10.1002\/nme.800","article-title":"Topology optimization of trusses using genetic algorithm, force method and graph theory","volume":"58","author":"Kaveh","year":"2003","journal-title":"Int. J. Numer. Methods Eng."},{"key":"mlstad69ffbib48","doi-asserted-by":"publisher","first-page":"645","DOI":"10.1002\/qua.20665","article-title":"Search for the ground states of Ising spin clusters by using the genetic algorithms","volume":"105","author":"Oda","year":"2005","journal-title":"Int. J. Quantum Chem."},{"key":"mlstad69ffbib49","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevB.85.012401","article-title":"Ground-state search in multicomponent magnetic systems","volume":"85","author":"Kumagai","year":"2012","journal-title":"Phys. Rev. B"},{"key":"mlstad69ffbib50","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1038\/s41578-021-00375-z","article-title":"Ferroelectric domain walls for nanotechnology","volume":"7","author":"Meier","year":"2022","journal-title":"Nat. Rev. Mater."},{"key":"mlstad69ffbib51","doi-asserted-by":"publisher","first-page":"432","DOI":"10.1038\/nnano.2015.339","article-title":"Magnetic domain walls as reconfigurable spin-wave nanochannels","volume":"11","author":"Wagner","year":"2016","journal-title":"Nat. Nanotechnol."},{"key":"mlstad69ffbib52","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1016\/0039-6028(96)00816-3","article-title":"A semi-empirical effective medium theory for metals and alloys","volume":"366","author":"Jacobsen","year":"1996","journal-title":"Surf. Sci."},{"key":"mlstad69ffbib53","doi-asserted-by":"crossref","DOI":"10.1021\/acs.jpcc.4c01704","article-title":"Adapting OC20-trained EquiformerV2 models for high-entropy materials","author":"Clausen","year":"2024"},{"key":"mlstad69ffbib54","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jpcc.6b03375","article-title":"Framework for scalable adsorbate\u2013adsorbate interaction models","volume":"120","author":"Hoffmann","year":"2016","journal-title":"J. Phys. Chem. C"},{"key":"mlstad69ffbib55","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1016\/j.joule.2018.12.015","article-title":"High-entropy alloys as a discovery platform for electrocatalysis","volume":"3","author":"Batchelor","year":"2019","journal-title":"Joule"},{"key":"mlstad69ffbib56","article-title":"A foundation model for atomistic materials chemistry","author":"Batatia","year":"2023"},{"key":"mlstad69ffbib57","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1038\/s41524-019-0189-9","article-title":"Machine-learned multi-system surrogate models for materials prediction","volume":"5","author":"Nyshadham","year":"2019","journal-title":"npj Comput. Mater."},{"key":"mlstad69ffbib58","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevB.102.075427","article-title":"Atomistic structure learning algorithm with surrogate energy model relaxation","volume":"102","author":"Mortensen","year":"2020","journal-title":"Phys. Rev. B"},{"key":"mlstad69ffbib59","doi-asserted-by":"publisher","first-page":"1080","DOI":"10.1021\/acs.jctc.2c01078","article-title":"Surrogate based genetic algorithm method for efficient identification of low-energy peptide structures","volume":"19","author":"Villard","year":"2023","journal-title":"J. Chem. Theory Comput."},{"key":"mlstad69ffbib60","article-title":"DeepDFT: neural message passing network for accurate charge density prediction","author":"J\u00f8rgensen","year":"2020"},{"key":"mlstad69ffbib61","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1007\/s002140050353","article-title":"Towards an order-N DFT method","volume":"99","author":"Fonseca Guerra","year":"1998","journal-title":"Theor. Chem. Acc."},{"key":"mlstad69ffbib62","doi-asserted-by":"publisher","first-page":"3594","DOI":"10.1021\/acs.jpclett.3c00242","article-title":"Adaptive design of alloys for CO2 activation and methanation via reinforcement learning Monte Carlo tree search algorithm","volume":"14","author":"Song","year":"2023","journal-title":"J. Phys. Chem. Lett."},{"key":"mlstad69ffbib63","first-page":"pp 69","article-title":"Surface composition and selectivity of alloy catalysts","volume":"vol 26","author":"Sachtler","year":"1977"},{"key":"mlstad69ffbib64","doi-asserted-by":"publisher","DOI":"10.1088\/2515-7655\/abf0f1","article-title":"Understanding disorder in oxide-based electrode materials for rechargeable batteries","volume":"3","author":"Christensen","year":"2021","journal-title":"J. Phys. Energy"},{"key":"mlstad69ffbib65","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.actamat.2016.09.032","article-title":"An assessment of the lattice strain in the CrMnFeCoNi high-entropy alloy","volume":"122","author":"Owen","year":"2017","journal-title":"Acta Mater."},{"key":"mlstad69ffbib66","doi-asserted-by":"crossref","DOI":"10.1109\/ICCV51070.2023.00371","article-title":"Segment anything","author":"Kirillov","year":"2023"},{"key":"mlstad69ffbib67","doi-asserted-by":"publisher","first-page":"7423","DOI":"10.1103\/PhysRevB.35.7423","article-title":"Interatomic interactions in the effective-medium theory","volume":"35","author":"Jacobsen","year":"1987","journal-title":"Phys. Rev. B"},{"key":"mlstad69ffbib68","doi-asserted-by":"publisher","DOI":"10.1088\/1361-648X\/aa680e","article-title":"The atomic simulation environment\u2014a Python library for working with atoms","volume":"29","author":"Larsen","year":"2017","journal-title":"J. Phys.: Condens. Matter"},{"key":"mlstad69ffbib69","article-title":"asap3 Python package v 3.12.12","author":"DTU FYSIK","year":"2022"},{"key":"mlstad69ffbib70","doi-asserted-by":"publisher","first-page":"338","DOI":"10.3390\/e20050338","article-title":"Pressure-volume work for metastable liquid and solid at zero pressure","volume":"20","author":"Imre","year":"2018","journal-title":"Entropy"},{"key":"mlstad69ffbib71","first-page":"pp 3438","article-title":"Measuring and relieving the over-smoothing problem for graph neural networks from the topological view","volume":"vol 34","author":"Chen","year":"2020"},{"key":"mlstad69ffbib72","article-title":"On the expressive power of geometric graph neural networks","author":"Joshi","year":"2023"},{"key":"mlstad69ffbib73","article-title":"Learning from protein structure with geometric vector perceptrons","author":"Jing","year":"2020"},{"key":"mlstad69ffbib74","first-page":"pp 1","article-title":"Universal readout for graph convolutional neural networks","author":"Navarin","year":"2019"},{"key":"mlstad69ffbib75","doi-asserted-by":"publisher","first-page":"1087","DOI":"10.1063\/1.1699114","article-title":"Equation of state calculations by fast computing machines","volume":"21","author":"Metropolis","year":"1953","journal-title":"J. Chem. Phys."},{"key":"mlstad69ffbib76","first-page":"pp 7652","article-title":"Sample factory: egocentric 3D control from pixels at 100000 fps with asynchronous reinforcement learning","author":"Petrenko","year":"2020"},{"key":"mlstad69ffbib77","doi-asserted-by":"publisher","first-page":"1108","DOI":"10.1016\/j.jpdc.2005.03.010","article-title":"MPI for Python","volume":"65","author":"Dalc\u00edn","year":"2005","journal-title":"J. Parallel Distrib. Comput."},{"key":"mlstad69ffbib78","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1007\/BF01349680","article-title":"Die konstitution der mischkristalle und die raumf\u00fcllung der atome","volume":"5","author":"Vegard","year":"1921","journal-title":"Z. Phys."},{"key":"mlstad69ffbib79","article-title":"Codebase for the ArtiSAN","author":"Elsborg","year":"2023"}],"container-title":["Machine Learning: Science and Technology"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad69ff","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad69ff\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad69ff","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad69ff\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad69ff\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad69ff\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad69ff\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad69ff\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,13]],"date-time":"2024-08-13T09:21:55Z","timestamp":1723540915000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad69ff"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,13]]},"references-count":79,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,8,13]]},"published-print":{"date-parts":[[2024,9,1]]}},"URL":"https:\/\/doi.org\/10.1088\/2632-2153\/ad69ff","relation":{"has-review":[{"id-type":"doi","id":"10.1088\/2632-2153\/AD69FF\/v2\/decision1","asserted-by":"object"},{"id-type":"doi","id":"10.1088\/2632-2153\/AD69FF\/v2\/response1","asserted-by":"object"},{"id-type":"doi","id":"10.1088\/2632-2153\/AD69FF\/v1\/review2","asserted-by":"object"},{"id-type":"doi","id":"10.1088\/2632-2153\/AD69FF\/v1\/review1","asserted-by":"object"},{"id-type":"doi","id":"10.1088\/2632-2153\/AD69FF\/v1\/decision1","asserted-by":"object"},{"id-type":"doi","id":"10.1088\/2632-2153\/AD69FF\/v2\/review1","asserted-by":"object"}],"has-preprint":[{"id-type":"doi","id":"10.26434\/chemrxiv-2024-32rxx","asserted-by":"object"}]},"ISSN":["2632-2153"],"issn-type":[{"value":"2632-2153","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,13]]},"assertion":[{"value":"ArtiSAN: navigating the complexity of material structures with deep reinforcement learning","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":"2024-05-11","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2024-07-31","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2024-08-13","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}