Machine-learning potentials for structurally and chemically complex MAB phases: strain hardening and ripplocation-mediated plasticity
arXiv:2503.16018 · doi:10.1016/j.matdes.2025.114307
Abstract
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 K, extrapolation grade and energy (force, stress) error analysis for 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} 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, WAlB able to retard fracture via ripplocations and twinning from 300 up to 1200~K.
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