diff --git a/README.md b/README.md
index dccd24abf3085d4846e24ab21314240c8764dff9..80429569a5d44c7e9ddcba279042bad33cdaff61 100644
--- a/README.md
+++ b/README.md
@@ -3,7 +3,7 @@
 
 Three main libraries are required to run the code : Dscribe (SOAP descriptors), Scikit-learn (Machine Learning tools) and ASE (Atomic Simulation Environment). 
 
-More precisely : 
+More precisely :   
 - python==3.10
 - ase==3.22.1  
 - scikit-learn==1.4.1
@@ -22,9 +22,9 @@ pip install -r requirements.txt
 
 The code builts Energy Adsorption Maps (EAMs) using machine learning.
 
-The file "run.py" (i) trains a model from a training set containing adsorption energies calculated on selected sites (".traj" file), and (ii) predicts adsorption energies of new sites located on a n $\times$ n regular grid. Results are written in the file "result.txt".
+The file "run.py" (i) trains a model from a training set containing adsorption energies calculated on selected sites (".traj" file), and (ii) predicts adsorption energies of new sites located on a $n$ $\times$ $n$ regular grid. Results are written in the file "result.txt".
 
-The ".traj" file contains data useful to describe the simulation box, the atomic positions, the grid (n) as well as parameters for the SOAP descriptors and the Gaussian Process Regression. 
+The ".traj" file contains data useful to describe the simulation box, the atomic positions, the grid ($n$) as well as parameters for the SOAP descriptors and the Gaussian Process Regression. 
 
 The code to create the Adsorption Enery Map ("Ag111_AEM.pdf", Working Example) is also given and uses the matplotlib package. Code execution must take place in the same directory as the file "result.txt" previously obtained.