Fig 4 of the article is added

metainfo.json

 {
   "authors": [
-    "Lucas Foppa",
-    "Thomas Purcell",
+    "Hassanzada, Qaem",
+    "Foppa, Lucas",
+    "Bartel, Christopher",
+    "Purcell, Thomas",
     "Sbailò, Luigi",
-    "Christopher Bartel",
     "Ghiringhelli, Luca M."
   ],
   "email": "ghiringhelli@fhi-berlin.mpg.de",
   "title": "Finding a tolerance factor to predict perovskite stability with SISSO",
-  "description": "In this tutorial...",
+  "description": "This tutorial shows how a tolerance factor for predicting perovskite stability can be learned from data with the sure-independece-screening-and-sparsifying-operator (SISSO) descriptor-identification approach.",
+  "notebook_name": "perovskites_tolerance_factor.ipynb",
   "url": "https://gitlab.mpcdf.mpg.de/nomad-lab/analytics-perovskite-tolerance-factor",
-  "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",
-  "updated": "2020-04-09",
+  "link": "https://analytics-toolkit.nomad-coe.eu/hub/user-redirect/notebooks/tutorials/perovskites_tolerance_factor.ipynb",
+  "link_public": "https://analytics-toolkit.nomad-coe.eu/public/user-redirect/notebooks/tutorials/perovskites_tolerance_factor.ipynb",
+  "link_paper": "https://advances.sciencemag.org/content/advances/5/2/eaav0693.full.pdf",
+  "link_doi_paper": "https://doi.org/10.1126/sciadv.aav0693",
+  "updated": "2021-12-14",
   "flags":{
     "featured": true,
-    "top_of_list": false
+    "top_of_list": false,
+    "paper": true
   },
   "labels": {
-    "application_keyword": [
-      "SISSO"
-    ],
     "application_section": [
-      "Materials property prediction"
+      "Timely artificial-intelligence applications to materials science"
+    ],
+    "category": [
+      "advanced_tutorial"
     ],
     "application_system": [
       "Perovskite"
     ],
-    "category": [
-      "Tutorial"
-    ],
-    "data_analytics_method": [
-      "SISSO"
+        "ai_methods": [
+      "Supervised learning",
+      "Classification",
+      "Symbolic regression",
+      "Compressed sensing",
+      "SISSO",
+      "Decision tree",
+      "Features selection",
+      "Atomic features"
     ],
     "platform": [
       "jupyter"