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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Atila, Achraf
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Topics
Publications (11/11 displayed)
- 2024Brittleness of metallic glasses dictated by their state at the fragile-to-strong transition temperature
- 2024Atomistic origins of deformation-induced structural anisotropy in metaphosphate glasses and its influence on mechanical properties
- 2024Predicting Grain Boundary Segregation in Magnesium Alloys: An Atomistically Informed Machine Learning Approach
- 2024The origin of phase separation in binary aluminosilicate glasses
- 2024Pressure-driven homogenization of lithium disilicate glassescitations
- 2023Influence of Structure and Topology on the Deformation Behavior and Fracture of Oxide Glasses ; Einfluss von Struktur und Topologie auf das Verformungsverhalten und den Bruch von Oxidgläsern
- 2023The origin of deformation induced topological anisotropy in silica glasscitations
- 2023The boson peak in silicate glasses: insight from molecular dynamicscitations
- 2023Thermally activated nature of synchro-Shockley dislocations in Laves phasescitations
- 2023Unveiling the mechanisms of motion of synchro-Shockley dislocations in Laves phasescitations
- 2022Atomistic insights into the mixed-alkali effect in phosphosilicate glassescitations
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document
Predicting Grain Boundary Segregation in Magnesium Alloys: An Atomistically Informed Machine Learning Approach
Abstract
Grain boundary (GB) segregation in magnesium (Mg) substantially influences its mechanical properties and performance. Atomic-scale modelling, typically using ab-initio or semi-empirical approaches, has mainly focused on GB segregation at highly symmetric GBs in Mg alloys, often failing to capture the diversity of local atomic environments and segregation energies, resulting in inaccurate structure-property predictions. This study employs atomistic simulations and machine learning models to systematically investigate the segregation behavior of common solute elements in polycrystalline Mg at both ground state and finite temperatures. The machine learning models accurately predict segregation thermodynamics by incorporating energetic and structural descriptors. We found that segregation energy and vibrational free energy follow skew-normal distributions, with hydrostatic stress, an indicator of excess free volume, emerging as an important factor influencing segregation tendency. The local atomic environment's flexibility, quantified by flexibility volume, is also crucial in predicting GB segregation. Comparing the grain boundary solute concentrations calculated via the Langmuir-McLean isotherm with experimental data, we identified a pronounced segregation tendency for Nd, highlighting its potential for GB engineering in Mg alloys. This work demonstrates the powerful synergy of atomistic simulations and machine learning, paving the way for designing advanced lightweight Mg alloys with tailored properties.