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∈{0,300,1200} K, extrapolation grade and energy (force, stress) error analysis for ≈3⋅106 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.
@article{arxiv.2503.16018,
title = {Machine-learning potentials for structurally and chemically complex MAB phases: strain hardening and ripplocation-mediated plasticity},
author = {Nikola Koutná and Shuyao Lin and Lars Hultman and Davide G. Sangiovanni and Paul H. Mayrhofer},
journal= {arXiv preprint arXiv:2503.16018},
year = {2025}
}