Data and code for "Phase transitions in inorganic halide perovskites from machine learning potentials: The impact of size, rate, and the underlying exchange-correlation functional" - Fransson, Erik; Wiktor, Julia; Erhart, Paul (Chalmers University of Technology, 2023)
This record contains databases with data from density functional theory calculations used for training a series of neuroevolution potentials (NEPs), which are also included here. Information is also included for how to access the databases and run the NEP models.

Databases
The *.db files are databases with the results from density functional theory (DFT) calculations. These are sqlite databases in ase format, see here for more information. The demo-database-access.py script illustrates the most basic access.

Models
The neuroevolution potential (NEP) models described in the publication can be found in the nep-*.txt files. They can be used in conjunction with the GPUMD package. The calorine package provides a Python interface to GPUMD.

Primitive structures
Several primitive structures in extended xyz format can be found in the *.xyz files. These structures have been relaxed using the NEP models included here. The demo-for-using-structures-and-models.py script illustrates how to access the structures and models.
Keywords: neuroevolution potential; GPUMD; halide perovskites; density functional theory; Physical Sciences; Fysik