[{"data":1,"prerenderedAt":99},["ShallowReactive",2],{"ki-evidenznetz-dossier-output-74e769e51baa4fd26d08fbf6-d9a9977d":3},{"edges":4,"entity":16,"fingerprint":55,"generatedAt":56,"neighbors":57,"schemaVersion":98},[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","101073486:output:74e769e51baa4fd26d08fbf6","https:\u002F\u002Fcordis.europa.eu\u002Fproject\u002Fid\u002F101073486","exact","cordis:101073486","edge:project-output:101073486:74e769e51baa4fd26d08fbf6","has_observed_output","output:74e769e51baa4fd26d08fbf6",{"attributes":17,"edgeCount":18,"id":15,"indexable":19,"label":34,"links":35,"slug":50,"sourceRefs":51,"type":54},{"citationCount":18,"isOpenAccess":19,"openAireId":20,"outputType":21,"persistentIdentifiers":22,"publicationDate":33},1,true,"doi_dedup___::407c76ee075be497094af7de7ef1b9ec","publication",[23,26,29,31],{"scheme":24,"value":25},"arXiv","2508.17792",{"scheme":27,"value":28},"doi","10.1088\u002F2515-7639\u002Fae2ba8",{"scheme":27,"value":30},"10.3929\u002Fethz-c-000791904",{"scheme":27,"value":32},"10.48550\u002Farxiv.2508.17792","2025-12-30","Universal machine learning potentials under pressure",[36,40,44,47],{"kind":37,"label":38,"url":39},"official_source","OpenAIRE","https:\u002F\u002Fapi.openaire.eu\u002Fgraph\u002Fv3\u002Fresearch-products\u002Fdoi_dedup___%3A%3A407c76ee075be497094af7de7ef1b9ec",{"kind":41,"label":42,"url":43},"persistent_identifier","DOI 10.1088\u002F2515-7639\u002Fae2ba8","https:\u002F\u002Fdoi.org\u002F10.1088\u002F2515-7639\u002Fae2ba8",{"kind":41,"label":45,"url":46},"DOI 10.3929\u002Fethz-c-000791904","https:\u002F\u002Fdoi.org\u002F10.3929\u002Fethz-c-000791904",{"kind":41,"label":48,"url":49},"DOI 10.48550\u002Farxiv.2508.17792","https:\u002F\u002Fdoi.org\u002F10.48550\u002Farxiv.2508.17792","output-74e769e51baa4fd26d08fbf6-d9a9977d",[52],{"dataset":7,"field":53,"sourceId":20,"url":39},"openaire_research_product_id","output","sha256:69c52e7592b49b0cd218440b37e600998903feb495c3a1797eb2ddb5037c1d69","2026-07-21T08:59:15.288Z",[58],{"attributes":59,"description":82,"edgeCount":83,"id":12,"indexable":19,"label":84,"links":85,"slug":92,"sourceRefs":93,"type":97},{"acronym":60,"code":61,"endDate":62,"frameworkProgramme":63,"funding":64,"knowledgeFlow":69,"research":73,"startDate":81},"EUSpecLab","101073486","2027-02-28","HORIZON",{"currency":65,"germanNetEuContributionCents":66,"participantEdgeCount":67,"status":68},"EUR","35824140",4,"linked",{"citingCountryCount":70,"citingInstitutionCount":67,"distinctCitingWorkCount":18,"firstObservedCitationDate":71,"lastObservedCitationDate":71,"observedCitationEdgeCount":18,"status":72},2,"2026-03-03","bounded_observation",{"citationDataOutputCount":74,"citedOutputCount":75,"firstPublicationDate":76,"lastPublicationDate":33,"openAccessOutputCount":74,"outputCount":74,"status":68,"totalCitations":77,"typeCounts":78},30,18,"2023-01-01",356,{"dataset":70,"other":18,"publication":79,"software":80},27,0,"2022-09-01","European Spectroscopy Laboratory to model the materials of the future",5,"EUSpecLab · European Spectroscopy Laboratory to model the materials of the future",[86,88],{"kind":37,"label":87,"url":10},"CORDIS",{"kind":89,"label":90,"url":91},"local_dossier","KI-Innovationspfad","\u002Ftools\u002Fki-innovationspfad\u002F?project=101073486","project-cordis-101073486-2d668cac",[94],{"dataset":95,"field":96,"sourceId":12,"url":10},"german-ai-innovation-path","cordis_project_id","project","ki-evidenznetz.entity.v1",1784624714605]