[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127012-en":3,"doc-seo-127012-105":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":4,"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},127012,2336474466412,"Ezra","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Capturing short-range order in high-entropy alloys with machine learning potentials","Chemical short-range order (SRO) governs how elements distribute within the solid-solution phase of metallic alloys, thereby shaping the energetic landscape for microstructural evolution. Establishing accurate chemistry–microstructure links requires atomistic models that span the relevant length scales while representing chemical-bond-driven motifs behind SRO. This work builds machine-learning potential training datasets for CrCoNi and evaluates how well they reproduce SRO and property-relevant quantities, including stacking-fault energy and phase stability. The study shows test-set energy accuracy can mislead property prediction and derives design principles for robust MLP construction for SRO in both crystal and liquid phases.","arXiv :2401 .06622v2 [ cond-mat .mtrl-sci ] 8 Jul 2024  \nCapturing short-range order in high-entropy alloys with machine learning potentials  \nYifan Cao 1 , Killian Sheriff1 , and Rodrigo Freitas 1 ∗  \n1 Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA  \nDated: July 10, 2024  \nAbstract  \nChemical short-range order (SRO) affects the distribution of elements throughout the solid-solution phase of metallic alloys, thereby modifying the background against which microstructural evolution occurs. Investigating such chemistrymicrostructure relationships requires atomistic models that act at the appropriate length scales while capturing the intricacies of chemical bonds leading to SRO. Here we consider various approaches for the construction of training datasets for machine learning potentials (MLPs) for CrCoNi and evaluate their performance in capturing SRO and its effects on materials quantities of relevance for mechanical properties, such as stacking-fault energy and phase stability. It is demonstrated that energy accuracy on test sets often does not correlate with accuracy in capturing material properties, which is fundamental in enabling large-scale atomistic simulations of metallic alloys with high physical fidelity. Based on this analysis we systematically derive design principles for the rational construction of MLPs that capture SRO in the crystal and liquid phases of alloys.  \nIn high-entropy alloys (HEAs) 1–3 multiple metallic elements are combined in nearly equal concentrations. This often leads to the stabilization of crystalline phases in which elements are distributed throughout the alloy in anearly-random fashion — namely, solid solution phases. This class of alloys has attracted substantial interest due to their mechanical properties. For example, extraordinary fracture resistance was observed in CrCoNi4 ,5 as the result of an unusual synergy of deformation mechanisms involving stacking-fault formation and phase transitions, as well as the conventional gliding of dislocations.  \nIt has been established that chemical short-range order (SRO)—i.e., the tendency of solid solutions to not be completely random—affects various chemistry–microstructure relationships that influence mechanical properties. For example, SRO has been shown to affect dislocation mobility6–8, grain boundaries9–11, stacking-fault energy 12–15 , and phase stability 16 . Consequently, significant experimental efforts have been made to characterize SRO and its effects on materials properties 11 , 13 , 14 , 17–22 . Connecting computational results to such experiments requires high-fidelity physical models capable of capturing the intricate nature of chemical bonds leading to SRO, while also accounting for the complexity of chemical motifs in HEAs23 ,24 .  \nIn a previous work (ref. 23) we have demonstrated that small-scale atomistic simulations, i.e., sizes typical of density-functional theory (DFT) calculations, are inadequate to properly capture SRO, leading to errors of up to 25% in the prediction of Warren-Cowley parameters. In the same work an approach for training machine learning interatomic potentials (MLPs) was demonstrated to capture SRO while simultaneously leading to an improvement in energy accuracy when compared to the state-of-the-art. Yet, fundamentally, such an approach consisted of a set of heuristics on the construction of the MLP, i.e. , a reasonable and practical approach for the construction of training sets without rigorous justification. Here we build on these  \n∗ Corresponding author ([rodrigof@mit.edu](rodrigof@mit.edu)).  \nresults and systematically derive the design principles for the rational construction of MLPs that capture SRO.  \nWhile much of the work on MLPs for HEAs has focused on their energy accuracy over test data sets7 ,25–37, here we focus instead on their performance in reproducing SRO and its effects on materials quantities of relevance for mechanical prope","cbCaid0stvJsFFDq","https://ap.wps.com/l/cbCaid0stvJsFFDq","pdf",23104659,1,11,"English","en",105,"# Abstract\n# Training strategy to manage chemical complexity\n## Focus on fcc CrCoNi solid-solution phase\n## Choice of Moment Tensor Potential (MTP)\n## Capturing chemically decorated coordination-shell motifs","[{\"question\":\"Why does chemical short-range order (SRO) matter in high-entropy alloys?\",\"answer\":\"SRO controls whether the solid-solution distribution is truly random, which influences chemistry–microstructure relationships tied to mechanical performance.\"},{\"question\":\"What property-relevant quantities are evaluated alongside SRO?\",\"answer\":\"The study evaluates stacking-fault energy and phase stability, linking SRO reproduction to quantities important for mechanical behavior.\"},{\"question\":\"Why is energy accuracy on test sets insufficient by itself?\",\"answer\":\"The results show that energy accuracy often does not correlate with accuracy in reproducing material properties that depend on SRO.\"}]","Capturing short-range order in high-entropy alloys with machine learning potentials | 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does chemical short-range order (SRO) matter in high-entropy alloys?","Question",{"text":75,"@type":76},"SRO controls whether the solid-solution distribution is truly random, which influences chemistry–microstructure relationships tied to mechanical performance.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What property-relevant quantities are evaluated alongside SRO?",{"text":80,"@type":76},"The study evaluates stacking-fault energy and phase stability, linking SRO reproduction to quantities important for mechanical behavior.",{"name":82,"@type":73,"acceptedAnswer":83},"Why is energy accuracy on test sets insufficient by itself?",{"text":84,"@type":76},"The results show that energy accuracy often does not correlate with accuracy in reproducing material properties that depend on 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