Information-entropy-driven generation of material-agnostic datasets for machine-learning interatomic potentials

This dataset contains the following information for the 8 elements from the paper, namely Be, C, Al, Sb, Te, W, Re and Os: About 30k structures (atomic positions, cell size) generated using the entropy-maximization method described in the paper. These are stored in the standard ASE atoms format, along with their respective DFT energies and forces and made available as pandas dataframes. The subset of structures used for training Atomic Cluster Expansion (ACE) models, i.e., the precise training datasets for all the above elements, and the corresponding test sets. The input files used to train potentials using the open-source Pacemaker code for all the elements. These are text files specifying the parameters used to train the models (basis set size, body-order of the expansion). The resulting trained potentials. Details of the DFT calculations and ACE parameterizations are provided in the paper.