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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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Guénolé, Julien
Engineering and Physical Sciences Research Council
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (22/22 displayed)
- 2025Grain boundary segregation spectrum in basal-textured Mg alloys: From solute decoration to structural transitioncitations
- 2024Defects in magnesium and its alloys by atomistic simulation: Assessment of semi-empirical potentialscitations
- 2024Microstructural defects in MAX phases
- 2024Exploring Solute Behavior and Texture Selection in Magnesium Alloys at the Atomistic Levelcitations
- 2024Predicting Grain Boundary Segregation in Magnesium Alloys: An Atomistically Informed Machine Learning Approach
- 2023Tailoring the Plasticity of Topologically Close‐packed Phases via the Crystals’ Fundamental Building Blockscitations
- 2023The origin of deformation induced topological anisotropy in silica glasscitations
- 2023Tailoring the Plasticity of Topologically Close‐Packed Phases via the Crystals’ Fundamental Building Blockscitations
- 2023Micro-/nano-structural defects in MAX phases
- 2023Revealing the nano-scale mechanisms of the limited non-basal plasticity in magnesium
- 2023Thermally activated nature of synchro-Shockley dislocations in Laves phasescitations
- 2023Unveiling the mechanisms of motion of synchro-Shockley dislocations in Laves phasescitations
- 2022Features of a nano-twist phase in the nanolayered Ti3AlC2 MAX phasecitations
- 2022Features of a nano-twist phase in the nanolayered Ti3AlC2 MAX phasecitations
- 2021Frank partial dislocation in Ti2AlC-MAX phase induced by matrix-Cu diffusioncitations
- 2021Exploring the transfer of plasticity across Laves phase interfaces in a dual phase magnesium alloycitations
- 2020In-situ observation of the initiation of plasticity by nucleation of prismatic dislocation loopscitations
- 2019Ti and its alloys as examples of cryogenic focused ion beam milling of environmentally-sensitive materialscitations
- 2019Elucidating the formation of Al–NBO bonds, Al–O–Al linkages and clusters in alkaline-earth aluminosilicate glasses based on molecular dynamics simulationscitations
- 2019Atomistic Simulations of Basal Dislocations Interacting with Mg$_{17}$Al$_{12}$ Precipitates in Mgcitations
- 2019Elucidating the formation of Al-NBO bonds, Al-O-Al linkages and clusters in alkaline-earth aluminosilicate glasses based on molecular dynamics simulationscitations
- 2015Atom probe informed simulations of dislocation–precipitate interactions reveal the importance of local interface curvaturecitations
Places of action
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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.