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Dataset for sustainability screening of 30,201 predicted equimolar high-entropy alloys

Alexandre NominéUniversité de LorraineAyyoub El-KasmiUniversité Mohammed VI PolytechniqueDanielle BeaulieuUniversity of AlbertaOuthmane OuahriUniversité Mohammed VI PolytechniqueThuy Huong NguyenUniversité de LorraineWassim AmzilUniversité Mohammed VI PolytechniqueAymane DroussiUniversité Mohammed VI PolytechniqueOleksandra KuksaUniversité de LorraineEirini KatsarouAristotle University of ThessalonikiChahrazed LabbaUniversité de LorraineAnne BoyerUniversité de LorraineH. HeneinUniversity of AlbertaThierry BelmonteInstitut Jean LamourFrançois RousseauUniversité de LorraineTuncay GürbüzGalatasaray UniversityElena MitrofanovaITMO UniversityAgnès SamperUniversité de LorrainePascal BolzThe University of QueenslandValentin A. MilichkoNew Uzbekistan UniversityOlga ChernoburovaUniversité de LorraineAlexandre ChagnesUniversité de LorraineMichel CathelineauUniversité de LorraineUroš CvelbarJožef Stefan InstituteJanez ZavašnikJožef Stefan Institute
Mendeley Datarepository2026
ABI

Аннотация

Version 2: Column names have been updated for improved clarity and consistency. No changes were made to the underlying data values. The dataset contains alloy-level descriptors for 30,201 candidate single-phase compositions identified from a palette of 40 elements. For each alloy, the dataset reports metrics related to supply concentration (HHI), environmental, social and governance risk (ESG), combined supply risk, companionability, production and reserve constraints, limiting elements, and CO2 and energy footprints under primary, recycled and actual scenarios (recycling-adjusted). The variables were derived from published sources and public databases, then aggregated at alloy level using arithmetic, probabilistic and weakest-link formulations. The dataset is intended for sustainable-by-design alloy selection, benchmarking of materials-screening pipelines, sensitivity analyses using alternative weighting strategies, and identification of bottleneck elements that constrain industrial scalability. Users can reuse the data to build their own composite indicators, test different thresholds, or couple sustainability descriptors with functional-property data.

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