[{"data":1,"prerenderedAt":89},["ShallowReactive",2],{"ki-evidenznetz-dossier-output-1365792f8f40c37a726bfeaf-72955ca8":3},{"edges":4,"entity":16,"fingerprint":43,"generatedAt":44,"neighbors":45,"schemaVersion":88},[5],{"evidence":6,"evidenceClass":11,"from":12,"id":13,"relation":14,"to":15},{"dataset":7,"field":8,"sourceId":9,"url":10},"german-ai-research-to-transfer-index","cordis_project_code","865855:output:1365792f8f40c37a726bfeaf","https:\u002F\u002Fcordis.europa.eu\u002Fproject\u002Fid\u002F865855","exact","cordis:865855","edge:project-output:865855:1365792f8f40c37a726bfeaf","has_observed_output","output:1365792f8f40c37a726bfeaf",{"attributes":17,"edgeCount":27,"id":15,"indexable":19,"label":28,"links":29,"slug":38,"sourceRefs":39,"type":42},{"citationCount":18,"isOpenAccess":19,"openAireId":20,"outputType":21,"persistentIdentifiers":22,"publicationDate":26},0,true,"doi_dedup___::941e3018233a3fbef56559043c9c45e8","dataset",[23],{"scheme":24,"value":25},"doi","10.18419\u002Fdarus-5544","2026-01-01",1,"Data for: Hydrogen diffusion in TiCr2Hx Laves phases: A combined ab initio and machine-learning-potential study",[30,34],{"kind":31,"label":32,"url":33},"official_source","OpenAIRE","https:\u002F\u002Fapi.openaire.eu\u002Fgraph\u002Fv3\u002Fresearch-products\u002Fdoi_dedup___%3A%3A941e3018233a3fbef56559043c9c45e8",{"kind":35,"label":36,"url":37},"persistent_identifier","DOI 10.18419\u002Fdarus-5544","https:\u002F\u002Fdoi.org\u002F10.18419\u002Fdarus-5544","output-1365792f8f40c37a726bfeaf-72955ca8",[40],{"dataset":7,"field":41,"sourceId":20,"url":33},"openaire_research_product_id","output","sha256:5935a9b29543ce0ac83b74ab0eaa817b8fbb9885d83764fc97f5f186bc049a88","2026-07-21T08:59:15.288Z",[46],{"attributes":47,"description":73,"edgeCount":57,"id":12,"indexable":19,"label":74,"links":75,"slug":82,"sourceRefs":83,"type":87},{"acronym":48,"code":49,"endDate":50,"frameworkProgramme":51,"funding":52,"knowledgeFlow":56,"research":63,"startDate":72},"Materials 4.0","865855","2025-12-31","H2020",{"currency":53,"germanNetEuContributionCents":54,"participantEdgeCount":27,"status":55},"EUR","200000000","linked",{"citingCountryCount":57,"citingInstitutionCount":58,"distinctCitingWorkCount":59,"firstObservedCitationDate":60,"lastObservedCitationDate":61,"observedCitationEdgeCount":59,"status":62},9,37,11,"2026-02-03","2026-07-01","bounded_observation",{"citationDataOutputCount":64,"citedOutputCount":65,"firstPublicationDate":66,"lastPublicationDate":67,"openAccessOutputCount":68,"outputCount":64,"status":55,"totalCitations":69,"typeCounts":70},73,48,"2021-05-20","2026-04-01",60,671,{"dataset":71,"other":18,"publication":68,"software":18},13,"2021-01-01","Advancing materials design by high-accuracy finite-temperature first principles calculations accelerated by machine learning potentials","Materials 4.0 · Advancing materials design by high-accuracy finite-temperature first principles calculations accelerated by machine learning potentials",[76,78],{"kind":31,"label":77,"url":10},"CORDIS",{"kind":79,"label":80,"url":81},"local_dossier","KI-Innovationspfad","\u002Ftools\u002Fki-innovationspfad\u002F?project=865855","project-cordis-865855-7473a39f",[84],{"dataset":85,"field":86,"sourceId":12,"url":10},"german-ai-innovation-path","cordis_project_id","project","ki-evidenznetz.entity.v1",1784624702043]