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

DOI

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)

Though offering unprecedented pathways to molecular dynamics (MD) simulations of technologically-relevant materials and conditions, machine-learning interatomic potentials (MLIPs) are typically trained for “simple” materials and properties with minor size effects. Our study of MAB phases (MABs)—alternating transition metal boride (MB) and group A element layers—exemplifies that MLIPs for complex materials can be fitted and used in a high-throughput fashion: for predicting structural and mechanical properties across a large chemical/phase/temperature space. Considering group 4–6 transition metal based MABs, with A=Al and the 222, 212, and 314 type phases, three MLIPs are trained and tested, including lattice and elastic constants calculations at temperatures T in {0,300,1200} K, extrapolation grade and energy (force, stress) error analysis for~= 3×10^6 ab initio MD snapshots. Subsequently, nanoscale tensile tests serve to quantify upper limits of strength and toughness attainable in single-crystal MABs at 300 K as well as their temperature evolution. In-plane tensile deformation is characterised by relatively high strength, {110}〈001〉 type slipping, and failure by shear banding. The response to [001] loading is softer, triggers work hardening, and failure by kinking and layer delamination. Furthermore, W2AlB2 able to retard fracture via ripplocations and twinning from 300 up to 1200 K.

Identifier
DOI https://doi.org/10.48436/5cc2j-sb797
Related Identifier IsSupplementTo https://doi.org/10.1016/j.matdes.2025.114307
Related Identifier IsVersionOf https://doi.org/10.48436/6aky6-qth12
Metadata Access https://researchdata.tuwien.ac.at/oai2d?verb=GetRecord&metadataPrefix=oai_datacite&identifier=oai:researchdata.tuwien.ac.at:5cc2j-sb797
Provenance
Creator Koutna, Nikola
Publisher TU Wien
Publication Year 2025
Funding Reference FWF Austrian Science Fund 013tf3c58 ROR 10.55776/RIC2714224 PREVENTING MATERIAL’S FAILURE UNDER EXTREME LOADS
Rights Creative Commons Attribution 4.0 International; https://creativecommons.org/licenses/by/4.0/legalcode
OpenAccess true
Contact tudata(at)tuwien.ac.at
Representation
Resource Type Dataset
Discipline Construction Engineering and Architecture; Engineering; Engineering Sciences; Materials Engineering; Materials Science and Engineering