[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119666-en":3,"doc-seo-119666-105":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},119666,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Deformation mechanisms of AlCoCrCuFeNi - A molecular dynamics and machine learning approach","High-entropy alloys (HEAs) are explored through the deformation response of the AlCoCrCuFeNi system using molecular dynamics simulations combined with machine learning. The study varies temperature, strain rate, and average grain size to reveal how partial dislocation interactions generate lattice disorders during tension and compression. It analyzes the influence of temperature, strain rate, and grain boundaries on plastic deformation behavior, dislocation density, lattice disorder, and von-Mises stress. A two-stage machine learning workflow is proposed to predict mechanical properties with reduced simulation time and validated accuracy, while learning interpretable property representations.","Materials Today Nano 31 (2025) 100662  \nContents lists available at ScienceDirect  \nMaterials Today Nano  \njournal [homepage: www.sciencedirect.com/journal/materials-today-nano](homepage: www.sciencedirect.com/journal/materials-today-nano)  \n| Deformation mechanisms of AlCoCrCuFeNi: A molecular dynamics and machine learning approach |  |  |  |\n| --- | --- | --- | --- |\n| Hoang-Giang Nguyen a,b,c, Sheng-Joue Young b, Thanh-Dung Led,e, Symeon Chatzinotasd, Te-Hua Fang a,f,*\u003Cbr>a Department of Mechanical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 807, Taiwan b Department of Electronic Engineering, National United University, Miaoli City, Taiwan\u003Cbr>c Faculty of Engineering, Kien Giang University, Kien Giang Province, Viet Nam\u003Cbr>d Interdisciplinary Centre for Security, Reliability, and Trust (SnT), University of Luxembourg, Luxembourg e Department of Electrical Engineering, ´Ecolede Technologie Sup´erieure, University of Qu´ebec, Montr´eal, Qu´ebec, Canada f Department of Fragrance and Cosmetic Science, Kaohsiung Medical University, Kaohsiung, 807, Taiwan |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Mechanical properties Machine learning Molecular dynamic Stress\u003Cbr>Dislocation density |  | High-entropy alloys (HEAs) distinguish themselves from other multi-component alloys through their unique nanostructures and mechanical properties. This study employs molecular dynamics (MD) simulations and machine learning to investigate the deformation mechanisms of AlCoCuCrFeNi HEA under varying temperatures, strain rates, and average grain sizes. The modeling results show that interactions between partial dislocations in AlCoCrCuFeNi HEA during tension and compression deformation cause various lattice disorders. The effect of temperature, strain rates, and grain boundaries on lattice disorder, plastic deformation behavior, dislocation density, and von-Mises stress (VMS) is disclosed. This study offers new insights into the atomic-scale deformation mechanisms governing the mechanical behavior of AlCoCrCuFeNi HEAs. It also presents a comprehensive workflow for predicting the mechanical properties of this HEA using machine learning models. The proposed approach provides several advantages, including significantly reduced simulation time and robust model validation. By employing the machine learning model trained in Stage 1, the time needed to simulate mechanical properties in Stage 2 is significantly decreased. Additionally, the framework ensures that the machine learning model effectively captures and understands the underlying representations of the mechanical properties of HEAs, thereby enhancing both the efficiency and accuracy of the predictions. |  |\n\n1. Introduction  \nThe pursuit of materials with superior mechanical properties has continually propelled human technological advancement. Discovering new metals and alloys has been central to this progress. Traditionally, alloys are classified by their dominant elemental component, such as Co-, Cr-, or Ni-based systems [1,2]. High-entropy alloys (HEAs) have emerged as a groundbreaking class of materials, exhibiting properties that transcend conventional multi-component or near-equiatomic alloy concepts. Comprising high concentrations of various elements with distinct crystal structures, HEAs can form stable single-phase solid solutions [3–5]. These multi-element alloys possess elevated configurational entropy in their random solution states, which favors the formation of simple solid solutions rather than complex multiphase  \nmicrostructures [6,7]. Unlike traditional alloy design focusing on the phase diagram’s corners, HEAs offer new pathways to create advanced materials with remarkable potential [8]. At elevated temperatures, atomic diffusion within high-entropy alloys (HEAs) occurs at a sluggish rate, resulting in high activation energy for grain growth and a slower phase transition process [9]. Furthermore, HEAs are ofte","cbCaimeb4DLvHPFM","https://ap.wps.com/l/cbCaimeb4DLvHPFM","pdf",34263495,1,24,"English","en",105,"# Introduction\n## High-entropy alloys and motivations\n## Microstructure and mechanical property evaluation methods\n## Indentation and scratching approaches","[{\"question\":\"What methods are used to study AlCoCrCuFeNi deformation mechanisms?\",\"answer\":\"The study uses molecular dynamics (MD) simulations together with machine learning to model and predict deformation behavior under different conditions.\"},{\"question\":\"Which factors are varied to investigate the deformation behavior?\",\"answer\":\"Temperature, strain rate, and average grain size are varied to determine their effects on lattice disorder, plastic deformation, dislocation density, and von-Mises stress.\"},{\"question\":\"How does the machine learning workflow improve efficiency?\",\"answer\":\"A two-stage approach trains a model in Stage 1 and uses it to accelerate property simulations in Stage 2, significantly reducing required simulation time while maintaining robust validation.\"}]","Deformation mechanisms of AlCoCrCuFeNi - A molecular dynamics and machine learning approach | PDF",1785725576,60,{"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},"deformation-mechanisms-of-alcocrcufeni-a-molecular-dynamics-and-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/deformation-mechanisms-of-alcocrcufeni-a-molecular-dynamics-and-machine-learning-approach/119666/",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},"What methods are used to study AlCoCrCuFeNi deformation mechanisms?","Question",{"text":75,"@type":76},"The study uses molecular dynamics (MD) simulations together with machine learning to model and predict deformation behavior under different conditions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which factors are varied to investigate the deformation behavior?",{"text":80,"@type":76},"Temperature, strain rate, and average grain size are varied to determine their effects on lattice disorder, plastic deformation, dislocation density, and von-Mises stress.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the machine learning workflow improve efficiency?",{"text":84,"@type":76},"A two-stage approach trains a model in Stage 1 and uses it to accelerate property simulations in Stage 2, significantly reducing required simulation time while maintaining robust validation.","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},[93,97,101,105,109,114,119,122,127,130,134],{"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":29,"slug":108},5,"Comic","comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"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"]