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.
