From 871b14f896a859f713cbc7df8bfa63734476c796 Mon Sep 17 00:00:00 2001
From: GAUDRY Emilie <emilie.gaudry@univ-lorraine.fr>
Date: Thu, 15 Aug 2024 12:49:15 +0000
Subject: [PATCH] Update file README.md
---
README.md | 6 +++---
1 file changed, 3 insertions(+), 3 deletions(-)
diff --git a/README.md b/README.md
index dccd24a..8042956 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.
--
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