DFT dataset for "Symmetry Breaking in the Superionic Phase of Silver-Iodide"

This dataset for the bulk AgI system, linked to the publication "Symmetry Breaking in the Superionic Phase of Silver-Iodide" [Phys. Rev. Lett. 134, 026306 (2025)], includes calculations performed using Quantum ESPRESSO with the PBE-D3 functional. It explores temperatures up to 600 K (800 K in some cases) at selective pressures through active learning. The data is categorized into training and testing sets, with configurations derived from MD trajectories of different phases (wurtzite, zincblende, rocksalt, etc.). This data is used for generating a machine learning force-field which is used for large scale simulations of the superionic transition in silver-iodide. Additionally, short ab initio trajectories are provided, integrated using the Verlet algorithm, which are used for validation of the machine learning force-field.

The checkpoints for the committee of the machine learning force field are available at: https://gitlab.com/amirhajibabaei/bulkagi