Update metainfo.json

metainfo.json

@@ -4,35 +4,34 @@
   ],
   "email": "langer@fhi-berlin.mpg.de",
   "title": "cmlkit: Toolkit for Machine Learning in Computational Condensed Matter Physics and Quantum Chemistry",
-  "description": "In this tutorial we will get to know cmlkit, a python package for specifying, evaluating, and optimising machine learning models, and use it to compete in the Nomad 2018 Kaggle challenge.",
+  "description": "In this tutorial, we will get to know cmlkit, a python package for specifying, evaluating, and optimising machine learning models, and use it to compete in the Nomad 2018 Kaggle challenge.",
   "url": "https://gitlab.mpcdf.mpg.de/nomad-lab/analytics-cmlkit",
   "link": "https://analytics-toolkit.nomad-coe.eu/hub/user-redirect/notebooks/tutorials/cmlkit.ipynb",
   "link_public": "https://analytics-toolkit.nomad-coe.eu/public/user-redirect/notebooks/tutorials/cmlkit.ipynb",
   "link_paper": "https://arxiv.org/abs/2003.12081",
 
-  "updated": "2020-03-26",
+  "updated": "2021-01-14",
   "flags":{
     "featured": true,
     "top_of_list": false
   },
   "labels": {
-    "application_keyword": [
-      "Formation energy prediction"
-    ],
     "application_section": [
       "Tutorials for artificial-intelligence methods"
     ],
     "application_system": [
-      "Group-III oxides"
+      "Transparent conducting oxides"
     ],
     "category": [
-      "intermediate_tutorial"
+      "advanced_tutorial"
     ],
-    "data_analytics_method": [
+    "ai_methods": [
+      "Supervised learning",
+      "Regression",
       "Kernel ridge regression",
       "SOAP",
       "MBTR",
-      "Symmetry Functions"
+      "Symmetry functions"
     ],
     "platform": [
       "jupyter"