FF-fit.demoinfo.yaml 1.3 KB
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{
"id": "5a09a48280996e0031366347",
"type": "demos",
"attributes": {
"title": "Complexity estimator for accurate-forces learning",
"logicalPath": "/data/shared/tutorialsNew/ff-fit/FF-fit.bkr",
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"authors": ["Fekete, Ádám", "Glielmo, Aldo", "Stella, Martina", "De Vita, Alessandro"],
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"editLink": "/notebook-edit/data/shared/tutorialsNew/ff-fit/FF-fit.bkr",
"isPublic": true,
"username": "tutorialsNew",
"description": "This tutorial makes use of Gaussian process (GP) regression in order to assess the complexity of a given system. This can be defined as the data set size necessary to the GP to predict a target property (eg. atomic forces, total energy of a configuration). The currently available data is the atomic forces in the mono-crystalline silicon at 300K, 1200K and 3000K.",
"created_at": "2017-11-13T13:56:37.859Z",
"updated_at": "2017-11-13T13:56:37.859Z",
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"user_update": "2017-05-15",
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"top_of_list": false,
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"featured": true,
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"labels" : {
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	"category" : ["Demo"],
	"platform" :  ["beaker"],
	"language" :  ["python", "javascript"],
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	"data_analytics_method" : ["Gaussian-Process Regression","GPR"],
	"application_keyword" : ["Elemental solids"],
	"application_system" :	["monocrystalline Si", "Si"],
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	"application_section" : ["Materials property prediction"],
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	"reference" : ["https://arxiv.org/abs/1611.03877"]
}
}
}