diff --git a/README.md b/README.md index 60693f892740550c55ba25db084e30c6899d039d..333a5195944a6d0ab4fe6e96a82ae38594c22872 100644 --- a/README.md +++ b/README.md @@ -22,7 +22,7 @@ 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 above the surface. 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.