2026-10-14 https://api.researchdata.se/oai-pmh

oai:researchdata.se:doi-10-5281-zenodo-8014365/0 2026-03-03 subject:ssif:103 subject:ssif:1 principal:slug:chalmers-university-of-technology

info:eu-repo/semantics/other http://purl.org/dc/dcmitype/Dataset https://doi.org/10.5281/zenodo.8014365 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" https://orcid.org/0000-0001-5262-3339 https://orcid.org/0000-0003-3395-1104 https://orcid.org/0000-0002-2516-6061 Physical Sciences Fysik 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. info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/ Chalmers University of Technology Chalmers tekniska högskola 2023-06-07T00:00:00Z