learning_atomic_charges.demoinfo.yaml 838 Bytes
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{
"title": "Learning atomic charges",
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"authors": ["Csányi, Gábor", "Kermode, James R."],
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"editLink": "/jupyter/cM/start/data/shared/afekete/tutorial/learning_atomic_charges.ipynb",
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"isPublic": true,
"username": "tutorialsNew",
"description": "In this tutorial, we will use Gaussian process regression, GPR (or equivalently, Kernel Ridge Regression, KRR) to train and predict charges of atoms in small organic molecules.", 
"created_at": "",
"updated_at": "",
"user_update": "2017-09-29",
"top_of_list": false,
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"featured": true,
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"labels" : { 
	"category" : ["Tutorial"],
	"platform" :  ["jupyter"],
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	"data_analytics_method" : ["Gaussian-Process Regression", "GPR", "Kernel Ridge Regression", "KRR"],
	"application_keyword": ["GDB molecular database"],
	"application_system" : ["GDB7"],
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	"application_section" : ["Organic molecules"]
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}
}