[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125863-en":3,"doc-seo-125863-105":31,"detail-sidebar-cat-0-en-105":93},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},125863,1099523882367,"Hazel","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Surface segregation in high-entropy alloys from alchemical machine learning","High-entropy alloys (HEAs) with near-equimolar mixtures of multiple metals are attractive for their mechanical properties and for catalysis, where synergistic component effects expand the design space. This work uses an alchemical machine-learning potential to model surface segregation tendencies across up to 25 transition metals. Starting from a bulk-trained model, physically inspired feature-space contraction is extended by adding a small fraction of defective surface and molten configurations, enabling accurate simulations of segregation in large and targeted HEA systems.","arXiv :2310 .07604v2 [ cond-mat .mtrl-sci ] 11 Jan 2024  \nSurface segregation in high-entropy alloys from alchemical machine learning  \nArslan Mazitov,1 Maximilian A. Springer,2 Nataliya Lopanitsyna,1 Guillaume Fraux,1 Sandip De,2 and Michele Ceriotti1  \n1) Laboratory of Computational Science and Modeling, Institute of Materials,´Ecole Polytechnique F´ed´erale de Lausanne, 1015 Lausanne, Switzerland  \n2) BASF SE, Carl-Bosch-Straße 38, 67056 Ludwigshafen, Germany  \nHigh-entropy alloys (HEAs), containing several metallic elements in near-equimolar proportions, have long been of interest for their unique mechanical properties. More recently, they have emerged as a promising platform for the development of novel heterogeneous catalysts, because of the large design space, and the synergistic effects between their components. In this work we use a machine-learning potential that can model simultaneously up to 25 transition metals to study the tendency of different elements to segregate at the surface of a HEA. We use as a starting point a potential that was previously developed using exclusively crystalline bulk phases, and show that, thanks to the physically-inspired functional form of the model, adding a much smaller number of defective configurations makes it capable of describing surface phenomena. We then present several computational studies of surface segregation, including both a simulation of a 25-element alloy, that provides a rough estimate of the relative surface propensity of the various elements, and targeted studies of CoCrFeMnNi and IrFeCoNiCu, which provide further validation of the model, and insights to guide the modeling and design of alloys for heterogeneous catalysis.  \nI. INTRODUCTION  \nCatalysts are widely used in modern industrial chemistry processes to lower the barriers and thus enhance the rates of a multitude of diverse chemical reactions. Among the many different classes of catalysts, a lot of attention has been recently devoted to high-entropy alloys (HEAs) . Initially introduced by Yeh 1 and Cantor2 for metallurgic and mechanical applications, HEAs were shown to exhibit promising catalytic3–7 and especially electrocatalytic8–10 behavior. The range of HEAs applications for catalysis includes decomposition of water for hydrogen production 11–22 , oxygen reduction 15,18,23–26 , methanol oxidation22,23,27–30 , reduction of CO2 and CO molecules22,31 , and decomposition of ammonia9 . The peculiar properties of HEAs are usually attributed to their multicomponent nature. It leads to lattice distortion32 and sluggish diffusion 33 effects, which kinetically stabilize the alloy. Additionally, the“cocktail effect”32,34–36 associated with the synergy between different elements, causes their mechanical and chemical behavior including their enhanced catalytic activity.  \nThe computational study and modeling of HEAs, and in particular their catalytic properties, is a promising approach to rapidly explore the enormous composition space. However, this is a challenging endeavor: Disordered alloys typically require large unit cells to obtain a statistically representative structure, what makes the ab initio simulations of HEAs computationally expensive and time consuming. Simulations of both elemental metals and HEAs based on empirical interatomic potentials are much faster, but are usually less accurate37–39 , especially when used to model multicomponent structures40 . Furthermore, most recent examples of traditional potentials for HEAs focus on a very narrow range of compositions  \nand are specifically optimized for a precise scientific question41,42 . Even machine learning interatomic potentials (MLIPs), which typically address this tradeoff by approximating the outcome of electronic structure calculations43–46 , cannot be applied straightforwardly. Many popular models based on atom-centered descriptors47,48 suffer from an exponential scaling of memory and computational requirements with respect to the number of disti","cbCaisRLTI4znTZa","https://ap.wps.com/l/cbCaisRLTI4znTZa","pdf",6208761,5,1,15,"English","en",105,"# Introduction\n## Catalysis and the role of high-entropy alloys\n## Computational challenges and limitations of existing potentials\n## Alchemical machine learning model for HEAs and its extension to surfaces\n## Surface segregation studies and validation","[{\"question\":\"Why are high-entropy alloys important for catalysis in this study?\",\"answer\":\"HEAs offer a large composition design space and can exhibit synergistic (“cocktail effect”) behaviors between different elements, which can enhance catalytic activity.\"},{\"question\":\"What is the main goal of the alchemical machine-learning approach here?\",\"answer\":\"To model how different elements segregate at the surfaces of high-entropy alloys, using a machine-learning potential that can handle up to 25 transition metals.\"},{\"question\":\"How is the bulk-trained model extended to describe surface and defective structures?\",\"answer\":\"By adding a small number (less than 20%) of surface and molten defective configurations to the training set, while relying on the physically inspired functional form and feature-space contraction for transferability.\"}]","Surface segregation in high-entropy alloys from alchemical machine learning | PDF",1785901649,38,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"surface-segregation-in-high-entropy-alloys-from-alchemical-machine-learning","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/surface-segregation-in-high-entropy-alloys-from-alchemical-machine-learning/125863/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"Why are high-entropy alloys important for catalysis in this study?","Question",{"text":77,"@type":78},"HEAs offer a large composition design space and can exhibit synergistic (“cocktail effect”) behaviors between different elements, which can enhance catalytic activity.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"What is the main goal of the alchemical machine-learning approach here?",{"text":82,"@type":78},"To model how different elements segregate at the surfaces of high-entropy alloys, using a machine-learning potential that can handle up to 25 transition metals.",{"name":84,"@type":75,"acceptedAnswer":85},"How is the bulk-trained model extended to describe surface and defective structures?",{"text":86,"@type":78},"By adding a small number (less than 20%) of surface and molten defective configurations to the training set, while relying on the physically inspired functional form and feature-space contraction for transferability.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":20,"slug":139},19,"General","general"]