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" doi-10-5281-zenodo-8014365-0 https://doi.org/10.5281/zenodo.8014365 Swedish National Data Service Svensk nationell datatjänst Landing page 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" doi-10-5281-zenodo-8014365-0 https://doi.org/10.5281/zenodo.8014365 Fransson, Erik Wiktor, Julia Swedish National Data Service Svensk nationell datatjänst Landing page 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. Access to data through an external actor. Data are freely accessible. Åtkomst till data via extern aktör. Data är fritt tillgängliga. openAccess