Publication date: Oct 23, 2023
High-entropy alloys (HEAs), containing several metallic elements in near-equimolar proportions, have long been of interest for their unique mechanical properties. More recently, they have emerged as a promising platform for the development of novel heterogeneous catalysts, because of the large design space, and the synergistic effects between their components. In this work we use a machine-learning potential that can model simultaneously up to 25 transition metals (d-block transition metals, excluding Tc, Cd, Re, Os and Hg) to study the tendency of different elements to segregate at the surface of a HEA. In this record, we provide a dataset HEA25S, containing 10000 bulk HEA structures (Dataset O), 2640 HEA surface slabs (Dataset A), together with 1000 bulk and 1000 surface slabs snapshots from the molecular dynamics (MD) runs (Datasets B and C), and 500 MD snapshots of the 25 elements Cantor-style alloy surface slabs. We also provide the HEA25-4-NN and HEA25S-4-NN final models, which were used in the study. Full description of both the dataset and the models can be found the reference paper below.
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|2.6 KiB||This README describes the HEA25S dataset containing compressed XYZ configurations (data.zip), HEA25-4-NN and HEA25S-4-NN models in the PyTorch model dict format (models.zip), and VASP settings used for calculations (vasp_settings.zip)|
|29.7 MiB||A zipped folder with XYZ files of the HEA25S dataset, containing 5 different classes of HEA data used in the study, spliced by the train, validation and test sets|
|2.5 MiB||A zipped folder with HEA25-4-NN and HEA25S-4-NN models in the PyTorch model dict format|
|1.3 KiB||A zipped folder with the VASP INCAR file|