[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121131-en":3,"doc-seo-121131-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121131,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Predicting Grain Boundary Segregation in Magnesium Alloys - An Atomistically Informed Machine Learning Approach","Grain boundary segregation in magnesium alloys strongly affects mechanical properties and performance, yet conventional atomic-scale modelling often targets only highly symmetric boundaries and can miss the diversity of local atomic environments and segregation energies. This work combines atomistic simulations with machine learning models to study segregation of common solutes in polycrystalline Mg at ground state and finite temperatures. Energetic and structural descriptors enable accurate predictions of segregation thermodynamics. Results show skew-normal trends for segregation energy and vibrational free energy, with hydrostatic stress and flexibility volume emerging as key factors. Langmuir–McLean comparisons with experiments highlight pronounced Nd segregation, supporting GB engineering and lightweight alloy design with tailored properties.","arXiv :2407 . 14148v1 [ cond-mat .mtrl-sci ] 19 Jul 2024  \nPredicting Grain Boundary Segregation in Magnesium Alloys: An Atomistically Informed Machine Learning Approach  \nZhuocheng Xie, 1, ∗ Achraf Atila,2,† Julien Gu´enol´e,3 Sandra Korte-Kerzel, 1 Talal Al-Samman, 1 and Ulrich Kerzel 1  \n1 Institute of Physical Metallurgy and Materials Physics,  \nRWTH Aachen University, 52056 Aachen, Germany  \n2 Department of Materials Science and Engineering,  \nSaarland University, 66123 Saarbr¨ucken, Germany  \n3 CNRS, Universit´e de Lorraine, Arts et M´etiers, LEM3, 57070 Metz, France  \nGrain 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.  \nKeywords: grain boundary segregation; magnesium alloys; atomistic simulation; machine learning  \nI. INTRODUCTION  \nDue to their low density and superior mechanical properties, magnesium (Mg) alloys are promising lightweight structural materials for enhancing sustainable, lowcarbon automotive applications [1, 2] . However, their widespread commercial adoption is hindered by substantial processing barriers, notably their limited formability, and ductility due to anisotropic plasticity and strong crystallographic texture [3, 4] . In response, recent years have seen significant efforts to improve the strength and ductility of Mg alloys. These advancements involve introducing alloying elements to reduce the disparity in critical resolved shear stresses between basal and non-basal dislocations [5, 6] and weakening the strong basal texture via grain boundary (GB) segregation [7, 8] .  \nSolute segregation at GBs has been widely reported to influence GB energy and mobility, subsequently affecting the texture development of Mg alloys [9, 10] . Understanding the atomistic origins of GB segregation phenomena is crucial for designing Mg alloys with specifically tailored properties. Nie et al. [11] reported periodic substitutional segregation of Gd and Zn solutes at twin boundaries in Mg alloys using high-resolution scanning transmission electron microscopy and attributed this periodic solute decoration to strain energy minimization using  \n∗ [xie@imm.rwth-aachen.de](xie@imm.rwth-aachen.de)[ ](xie@imm.rwth-aachen.de)† [achraf.atila@uni-saarland.de](achraf.atila@uni-saarland.de)  \nthe density functional theory (DFT) calculations. The pinning effect of this ordered solute segregation on twin boundaries leads to annealing strengthens of these Mg alloys. Similar atomic-resolution electron microscopic approaches have been carried out on other highly symmetric tilt GBs in Mg ","cbCailxBAVIalnLo","https://ap.wps.com/l/cbCailxBAVIalnLo","pdf",15962190,1,50,"English","en",105,"# Introduction\n## Motivation: lightweight Mg alloys and processing barriers\n## Role of solute segregation at grain boundaries\n## Experimental characterization of solute concentration\n## Atomistic modelling approaches and prior computational work","[{\"question\":\"Why is grain boundary segregation important in magnesium alloys?\",\"answer\":\"Grain boundary segregation alters grain boundary energy and mobility, influencing texture development and thereby mechanical properties and performance.\"},{\"question\":\"How does the study predict segregation thermodynamics?\",\"answer\":\"It uses atomistic simulations together with machine learning models that incorporate energetic and structural descriptors to predict segregation behavior at ground state and finite temperatures.\"},{\"question\":\"Which factors were identified as influencing segregation tendency?\",\"answer\":\"Segregation energy and vibrational free energy follow skew-normal distributions, while hydrostatic stress and flexibility volume (local atomic environment flexibility) strongly affect segregation tendency.\"}]","Predicting Grain Boundary Segregation in Magnesium Alloys - An Atomistically Informed Machine Learning Approach | PDF",1785733961,126,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"predicting-grain-boundary-segregation-in-magnesium-alloys-an-atomistically-informed-machine-learning-approach","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/predicting-grain-boundary-segregation-in-magnesium-alloys-an-atomistically-informed-machine-learning-approach/121131/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is grain boundary segregation important in magnesium alloys?","Question",{"text":75,"@type":76},"Grain boundary segregation alters grain boundary energy and mobility, influencing texture development and thereby mechanical properties and performance.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the study predict segregation thermodynamics?",{"text":80,"@type":76},"It uses atomistic simulations together with machine learning models that incorporate energetic and structural descriptors to predict segregation behavior at ground state and finite temperatures.",{"name":82,"@type":73,"acceptedAnswer":83},"Which factors were identified as influencing segregation tendency?",{"text":84,"@type":76},"Segregation energy and vibrational free energy follow skew-normal distributions, while hydrostatic stress and flexibility volume (local atomic environment flexibility) strongly affect segregation tendency.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":21,"slug":113},6,"Technology","technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]