Machine-learning potentials for structurally and chemically complex MAB phases: strain hardening and ripplocation-mediated plasticity
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| Format: | Preprint |
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2025
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| _version_ | 1866915375612952576 |
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| author | Koutná, Nikola Lin, Shuyao Hultman, Lars Sangiovanni, Davide G. Mayrhofer, Paul H. |
| author_facet | Koutná, Nikola Lin, Shuyao Hultman, Lars Sangiovanni, Davide G. Mayrhofer, Paul H. |
| contents | 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 $\approx{3\cdot10^6}$ 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}$\langle001\rangle$ 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_16018 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine-learning potentials for structurally and chemically complex MAB phases: strain hardening and ripplocation-mediated plasticity Koutná, Nikola Lin, Shuyao Hultman, Lars Sangiovanni, Davide G. Mayrhofer, Paul H. Materials Science 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 $\approx{3\cdot10^6}$ 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}$\langle001\rangle$ 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. |
| title | Machine-learning potentials for structurally and chemically complex MAB phases: strain hardening and ripplocation-mediated plasticity |
| topic | Materials Science |
| url | https://arxiv.org/abs/2503.16018 |