Research data for 'How Realistic are Idealized Copper Surfaces? A Machine Learning Study of Rough Copper-Water Interfaces'

This dataset supports the paper 'How Realistic are Idealized Copper Surfaces? A Machine Learning Study of Rough Copper-Water Interfaces'. The paper can be found under the following doi: https://doi.org/10.1021/acsmaterialsau.5c00174 The following files are provided: Database.zip: Database containing the structures used for training and testing together with the DFT energy and force labels. RoughSurfaces.zip: Contains all 46 rough copper structures including water on top. ActiveLearning.zip: Contains a python script, which was used to extract high uncertainty surface environments into smaller boxes and the corresponding lammps input file to perform the MD. Potentials.zip: Contains all potentials, which have been used in this work. UMAP.zip: Contains the coordinates of all the points in the UMAP figure. TrajectoryMD.zip: Contains the exemplary trajectory of one of the rough copper-water interfaces, which was also used to generate the UMAP data.