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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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Korte-Kerzel, Sandra
RWTH Aachen University
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (20/20 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
- 2024Understanding the damage initiation and growth mechanisms of two DP800 dual phase grades
- 2024Predicting Grain Boundary Segregation in Magnesium Alloys: An Atomistically Informed Machine Learning Approach
- 2023Laves phases in Mg-Al-Ca alloys and their effect on mechanical properties
- 2023Tailoring the Plasticity of Topologically Close‐Packed Phases via the Crystals’ Fundamental Building Blockscitations
- 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
- 2022Synergistic effects of solutes on active deformation modes, grain boundary segregation and texture evolution in Mg-Gd-Zn alloyscitations
- 2021Exploring the transfer of plasticity across Laves phase interfaces in a dual phase magnesium alloycitations
- 2020Ni–Cr–Al Alloy for neutron scattering at high pressurescitations
- 2020Ni–Cr–Al Alloy for neutron scattering at high pressurescitations
- 2019Ti and its alloys as examples of cryogenic focused ion beam milling of environmentally-sensitive materialscitations
- 2019Modelling of differential scanning calorimetry heating curves for precipitation and dissolution in an Al-Mg-Sicitations
- 2019Global and High-Resolution Damage Quantification in Dual-Phase Steel Bending Samples with Varying Stress Statescitations
- 2019Large-area, high-resolution characterisation and classification of damage mechanisms in dual-phase steel using deep learningcitations
- 2019Atomistic Simulations of Basal Dislocations Interacting with Mg$_{17}$Al$_{12}$ Precipitates in Mgcitations
- 2018Dislocations and Plastic Deformation in MgO Crystals: A Reviewcitations
- 2014Intrinsic and extrinsic size effects in the deformation of amorphous CuZr/nanocrystalline Cu nanolaminatescitations
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.