Update tutorials.json

Add nn_regression tutorial in json file

tutorials.json

@@ -747,7 +747,7 @@
       "email": "leitherer@fhi-berlin.mpg.de",
       "title": "ARISE - Robust recognition and exploratory analysis of crystal structures via Bayesian-deep-learning",
       "description": "In this tutorial we will give an introduction to ARISE (Leitherer, Ziletti, Ghiringhelli arXiv:2103.09777).",
-      "url": "https://gitlab.mpcdf.mpg.de/nomad-lab/analytics-tutorial-template",
+      "url": "https://gitlab.mpcdf.mpg.de/nomad-lab/analytics-arise",
       "link": "https://analytics-toolkit.nomad-coe.eu/hub/user-redirect/notebooks/tutorials/ARISE.ipynb",
       "link_public": "https://analytics-toolkit.nomad-coe.eu/public/user-redirect/notebooks/tutorials/ARISE.ipynb",
       "link_paper": "https://arxiv.org/abs/2103.09777",
@@ -784,6 +784,45 @@
           "jupyter"
         ]
       }
+    },
+    {
+      "authors": [
+        "Leitherer, Andreas",
+        "Sbailò, Luigi",
+        "Ghiringhelli, Luca M."
+      ],
+      "email": "leitherer@fhi-berlin.mpg.de",
+      "title": "Hands-on tutorial: Regression using multilayer perceptrons",
+      "description": "In this tutorial we will use the ElemNet neural network architecture (https://github.com/NU-CUCIS/ElemNet) to predict the volume per atom of inorganic compounds, where the open quantum materials database (OQMD) is used as a resource (specifically, the data is taken from Ward et. al., npj Comput. Mater. 2, 16028 (2016)).",
+      "url": "https://gitlab.mpcdf.mpg.de/nomad-lab/analytics-nn-regression",
+      "link": "https://analytics-toolkit.nomad-coe.eu/hub/user-redirect/notebooks/tutorials/nn_regression.ipynb",
+      "link_public": "https://analytics-toolkit.nomad-coe.eu/public/user-redirect/notebooks/tutorials/nn_regression.ipynb",
+      "updated": "2020-04-09",
+      "flags":{
+        "featured": true,
+        "top_of_list": false
+      },
+      "labels": {
+        "application_keyword": [
+          "Dee neural networks",
+          "Descriptors"
+        ],
+        "application_section": [
+          "Materials property prediction"
+        ],
+        "application_system": [
+          "Inorganic compounds taken from the OQMD database"
+        ],
+        "category": [
+          "Tutorial"
+        ],
+        "data_analytics_method": [
+          "Neural networks"
+        ],
+        "platform": [
+          "jupyter"
+        ]
+      }
     }
   ]
 }