Machine-learning potentials for structurally and chemically complex MAB phases: strain hardening and ripplocation-mediated plasticity

The data coresponds to the publication Machine-learning potentials for structurally and chemically complex MAB phases: strain hardening and ripplocation-mediated plasticity, by Nikola Koutná, Shuyao Lin, Lars Hultman, Davide G. Sangiovanni, Paul H. Mayrhoferaccessible at https://doi.org/10.1016/j.matdes.2025.114307

Methodology

The methods used to produce the data are described in the publication

Contents

The zip file contains a README file and 3 folders with various text files:

MABs_structures: relaxed structures in the VASP POSCAR format (https://www.vasp.at/wiki/index.php/POSCAR)

MLIPs: machine-learning interatomic potentials in the mlip-2 format (https://gitlab.com/ashapeev/mlip-2) and the corresponding training sets in the cfg format (compatible with the mlip-2 package)

Raw_data_from_tables: calculated lattice parameters, elastic constants, and mechanical properties, as listed in Tab.1-3 in the publication (https://doi.org/10.1016/j.matdes.2025.114307)