diff --git a/www-root/userapi/demos/index.json b/www-root/userapi/demos/index.json index 6791d2a14e50f3c50c2446321bf34253919d9efe..6d496d6a901ca796a24b30e59f88f17f20c45221 100644 --- a/www-root/userapi/demos/index.json +++ b/www-root/userapi/demos/index.json @@ -1003,6 +1003,53 @@ }, "id": "/custom-analytics-example/custom-analytics-example.demoinfo.yaml", "type": "demos" + }, + { + "attributes": { + "authors": [ + "Ziletti, Angelo", + "Kumar, Devinder", + "Scheffler, Matthias", + "Ghiringhelli, Luca" + ], + "created_at": "2019-01-23T11:56:28.881Z", + "description": "In this notebook, we use a machine learning-based approach to automatically classify structures by crystal symmetry; first, we represent crystals by a diffraction image, then use a neural network for classification. The notebook allows to reproduce the main results of: A. Ziletti, D. Kumar, M. Scheffler and L. M. Ghiringhelli, Nature Communications 9, 2775 (2018)", + "editLink": "/beaker/cM/start/home/beaker/tutorials/insightful_class_deep_learning_nature_comm2018.bkr?image=analytics-toolkit.nomad-coe.eu:5509/ziletti/face-of-crystals-2017:v2.0.3-squashed", + "featured": true, + "isPublic": true, + "labels": { + "application_keyword": [ + "Crystals", + "Structure" + ], + "application_section": [ + "Structure classification", + "Solids" + ], + "category": [ + "Demo" + ], + "data_analytics_method": [ + "Neural networks", + "Deep learning" + ], + "language": [ + "python", + "javascript" + ], + "platform": [ + "beaker" + ] + }, + "logicalPath": "/beaker/cM/start/home/beaker/tutorials/insightful_class_deep_learning_nature_comm2018.bkr?image=analytics-toolkit.nomad-coe.eu:5509/ziletti/face-of-crystals-2017:v2.0.3-squashed", + "title": "Insightful classification of crystal structures using deep learning", + "top_of_list": false, + "updated_at": "2019-01-23T11:56:28.881Z", + "user_update": "2019-01-23", + "username": "tutorialsNew" + }, + "id": "", + "type": "demos" } ] } \ No newline at end of file