Update metainfo.json

metainfo.json

@@ -5,33 +5,34 @@
     "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)).",
+  "title": "Introduction to multilayer perceptrons (deep neural networks)",
+  "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-tutorial-template",
   "link": "https://analytics-toolkit.nomad-coe.eu/hub/user-redirect/notebooks/tutorials/{tutorial}.ipynb",
   "link_public": "https://analytics-toolkit.nomad-coe.eu/public/user-redirect/notebooks/tutorials/{tutorial}.ipynb",
   "link_video": "https://www.youtube.com/watch?v=U0lI5n8Hleo",
-  "updated": "2020-04-09",
+  "updated": "2021-01-29",
   "flags":{
     "featured": true,
     "top_of_list": false
   },
   "labels": {
-    "application_keyword": [
-      "Neural networks / deep learning",
-      "Descriptors"
-    ],
     "application_section": [
       "Materials property prediction"
     ],
     "application_system": [
-      "Inorganic compounds taken from the OQMD database"
+      "Inorganic compounds",
+      "OQMD database"
     ],
     "category": [
       "beginner_tutorial"
     ],
-    "data_analytics_method": [
-      "Neural networks"
+    "ai_methods": [
+      "Supervised learning",
+      "Regression",
+      "Neural networks",
+      "Deep neural networks",
+      "Atomic features"
     ],
     "platform": [
       "jupyter"