Predicting grain boundary segregation in magnesium alloys: An atomistically informed machine learning approach
Grain boundary (GB) segregation substantially influences the mechanical properties and performance of magnesium (Mg). Atomic-scale modeling, 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...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-06-01
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| Series: | Journal of Magnesium and Alloys |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2213956725001124 |
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| author | Zhuocheng Xie Achraf Atila Julien Guénolé Sandra Korte-Kerzel Talal Al-Samman Ulrich Kerzel |
| author_facet | Zhuocheng Xie Achraf Atila Julien Guénolé Sandra Korte-Kerzel Talal Al-Samman Ulrich Kerzel |
| author_sort | Zhuocheng Xie |
| collection | DOAJ |
| description | Grain boundary (GB) segregation substantially influences the mechanical properties and performance of magnesium (Mg). Atomic-scale modeling, 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 0 K 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. |
| format | Article |
| id | doaj-art-4716fc4cefdf4a659fb935a1fcc7ed36 |
| institution | Kabale University |
| issn | 2213-9567 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Journal of Magnesium and Alloys |
| spelling | doaj-art-4716fc4cefdf4a659fb935a1fcc7ed362025-08-20T03:50:31ZengKeAi Communications Co., Ltd.Journal of Magnesium and Alloys2213-95672025-06-011362636265010.1016/j.jma.2025.03.021Predicting grain boundary segregation in magnesium alloys: An atomistically informed machine learning approachZhuocheng Xie0Achraf Atila1Julien Guénolé2Sandra Korte-Kerzel3Talal Al-Samman4Ulrich Kerzel5Corresponding authors.; Institut für Metallkunde und Materialphysik, RWTH Aachen University, 52056 Aachen, GermanyCorresponding authors.; Department of Materials Science and Engineering, Saarland University, 66123 Saarbrücken, Germany; Federal Institute of Materials Research and Testing (BAM), Unter den Eichen 87, Berlin 12205, GermanyCNRS, Université de Lorraine, Arts et Métiers, LEM3, 57070 Metz, FranceInstitut für Metallkunde und Materialphysik, RWTH Aachen University, 52056 Aachen, GermanyInstitut für Metallkunde und Materialphysik, RWTH Aachen University, 52056 Aachen, GermanyInstitut für Metallkunde und Materialphysik, RWTH Aachen University, 52056 Aachen, GermanyGrain boundary (GB) segregation substantially influences the mechanical properties and performance of magnesium (Mg). Atomic-scale modeling, 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 0 K 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.http://www.sciencedirect.com/science/article/pii/S2213956725001124Grain boundary segregationMagnesium alloysAtomistic simulationMachine learning |
| spellingShingle | Zhuocheng Xie Achraf Atila Julien Guénolé Sandra Korte-Kerzel Talal Al-Samman Ulrich Kerzel Predicting grain boundary segregation in magnesium alloys: An atomistically informed machine learning approach Journal of Magnesium and Alloys Grain boundary segregation Magnesium alloys Atomistic simulation Machine learning |
| title | Predicting grain boundary segregation in magnesium alloys: An atomistically informed machine learning approach |
| title_full | Predicting grain boundary segregation in magnesium alloys: An atomistically informed machine learning approach |
| title_fullStr | Predicting grain boundary segregation in magnesium alloys: An atomistically informed machine learning approach |
| title_full_unstemmed | Predicting grain boundary segregation in magnesium alloys: An atomistically informed machine learning approach |
| title_short | Predicting grain boundary segregation in magnesium alloys: An atomistically informed machine learning approach |
| title_sort | predicting grain boundary segregation in magnesium alloys an atomistically informed machine learning approach |
| topic | Grain boundary segregation Magnesium alloys Atomistic simulation Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2213956725001124 |
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