Update index.json

www-root/userapi/demos/index.json

@@ -950,6 +950,54 @@
       "id": "5a09a48180996e0031366343",
       "type": "demos"
     },
+    {
+      "attributes": {
+        "authors": [
+           “Xiangyue Liu", 
+           “Christopher Sutton", 
+           “Luca M. Ghiringhelli", 
+           “Takenori Yamamoto", 
+           “Yury Lysogorskiy", 
+           “Lars Blumenthal", 
+           “Thomas Hammerschmidt", 
+           “Jacek Golebiowski", 
+           “Angelo Ziletti", 
+           “Matthias Scheffler" 
+        ],
+        "created_at": "",
+        "description": "The NOMAD Kaggle competition in 2018 is crowd-sourced data analytics competition with Kaggle, to develop or apply data analytics models for the prediction of the formation energy and the bandgap energy to facilitate the discovery of new transparent conductors. In this tutorial we examine the performance of the winning representations n-grams and SOAP combined with different regression models, including kernel ridge regression and neural network.",
+        "editLink": "/jupyterhub/user/user-redirect/notebooks/tutorials/kaggle-competetion/kaggle_competetion.ipynb",
+        "featured": true,
+        "isPublic": true,
+        "labels": {
+          "application_keyword": [
+            "Nomad Competetion"
+          ],
+          "application_section": [
+            "Materials property prediction"
+          ],
+          "application_system": [
+            "Transparent conducting oxide"
+          ],
+          "category": [
+            "Tutorial"
+          ],
+          "data_analytics_method": [
+            "n-grams, SOAP, KRR, NN"
+          ],
+          "platform": [
+            "jupyter"
+          ]
+        },
+        "title": "NOMAD 2018 Kaggle competition",
+        "top_of_list": false,
+        "updated_at": "",
+        "user_update": "2019-03-29",
+        "username": "tutorialsNew"
+      },
+      "id": "",
+      "type": "demos"
+    },
     {
       "attributes": {
         "authors": [