Commit 31df33ad authored by Luca Ghiringhelli's avatar Luca Ghiringhelli

Added jupyter

parent d06f9a6b
{
"title": "Learning atomic charges",
"authors": ["Gábor Csányi", "James R. Kermode"],
"editLink": "/jupyter/notebooks/shared/afekete/tutorial/learning_atomic_charges.ipynb",
"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,
"labels" : {
"category" : ["Tutorial"],
"platform" : ["jupyter"],
"data_analytics_method" : ["Gaussian Process Regression", "Kernel Ridge Regression", "KRR"],
"application_keyword": ["GDB9 dataset", "Small Organic molecules"]
}
}
{
"title": "Grain boundaries of alpha-Fe tutorial",
"authors": ["Ádám Fekete", "Martina Stella", "Henry Lambert", "Alessandro De Vita", "Gábor Csányi"],
"editLink": "/jupyter/notebooks/shared/afekete/tutorial/GB_tutorial_Berlin_2017.ipynb",
"isPublic": true,
"username": "tutorialsNew",
"description": "In this tutorial we will be using a machine learning method (clustering) to analyse results of Grain Boundary (GB) calculations of alpha-iron. Along the way we will learn about different methods to describe local atomic environment in order to calculate properties of GBs. We will use these properties to separate the different regions of the GB using clustering methods. Finally we will determine how the energy of the GB is changing according to the angle difference of the regions.",
"created_at": "",
"updated_at": "",
"user_update": "2017-09-29",
"top_of_list": false,
"labels" : {
"category" : ["Tutorial"],
"platform" : ["jupyter"],
"data_analytics_method" : ["Clustering"],
"application_section" : ["Structure Similarity"],
"application_keyword": ["Iron", "Grain boundaries"]
}
}
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