diff --git a/README.md b/README.md
index eb1c9d6ccf28011d25a21c2ac6eadba50a176aec..7d8ac28173b6ce8bfd9bdb54e0033ab61f1f89e2 100644
--- a/README.md
+++ b/README.md
@@ -1 +1 @@
-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)).
+In this tutorial, we discuss how multilayer perceptrons, a standard neural-network architecture, can be employed for regression tasks. Specifically, we will use the ElemNet neural-network architecture to predict the volume per atom of inorganic compounds, where the Open Quantum Materials Database (OQMD) is used as a resource.
diff --git a/metainfo.json b/metainfo.json
index 3ecde6a5a12cb81e2db5c80a22c1d4986d93ae03..366e6705e96bb83e0aea07f65aedab7084bda5c1 100644
--- a/metainfo.json
+++ b/metainfo.json
@@ -6,7 +6,7 @@
   ],
   "email": "leitherer@fhi-berlin.mpg.de",
   "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)).",
+  "description": "In this tutorial, we discuss how multilayer perceptrons, a standard neural-network architecture, can be employed for regression tasks. Specifically, we will use the ElemNet neural-network architecture to predict the volume per atom of inorganic compounds, where the Open Quantum Materials Database (OQMD) is used as a resource.",
   "notebook_name": "nn_regression.ipynb",
   "url": "https://gitlab.mpcdf.mpg.de/nomad-lab/analytics-tutorial-template",
   "link": "https://analytics-toolkit.nomad-coe.eu/hub/user-redirect/notebooks/tutorials/nn_regression.ipynb",