Machine-learning potentials for structurally and chemically complex MAB phases: Strain hardening and ripplocation-mediated plasticity
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 ⋅ 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, W 2 AlB 2 able to retard fracture via ripplocations and twinning from 300 up to 1200 K. • Semi-automatic dataset generation for the training of ML potentials by simulating series of atomic-scale mechanical tests triggering growth of realistic defective environments (e.g., stacking faults, voids, surfaces). • Identification of two general deformation mechanisms in MAB phases: (i) 110〈001〉type slipping and failure by shear banding subject to in-plane loading; (ii) layer buckling and failure by kinking and delamination subject to out-ot-plane loading. • Design suggestions: W2AlB2 offers outstanding toughness via strain-activated nucleation of ripplocations and mechanical twins, Ta2AlB2 and W3AlB4 promote stacking fault formation, and Ti3AlB4 provides the highest theoretical tensile strength.
Authors
- Davide G. Sangiovanni (ORCID: https://orcid.org/0000-0002-1379-6656)
- Nikola Koutná (ORCID: https://orcid.org/0000-0001-7901-4736)
- Shuyao Lin (ORCID: https://orcid.org/0000-0003-4979-4174)
- P.H. Mayrhofer (ORCID: https://orcid.org/0000-0001-7328-9333)
- Lars Hultman (ORCID: https://orcid.org/0000-0002-2837-3656)
Institutions
- Linköping University (SE)
- Ming Chi University of Technology (TW)
- TU Wien (AT)
Publication Details
- Journal
- Materials & Design
- Published
- 2025-07-05
- DOI
- https://doi.org/10.1016/j.matdes.2025.114307
- Citations
- 3
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 1.55