20.500.12556/DiRROS-29321 Inverse design of high-entropy superalloys using machine learning and generative artificial Intelligence We introduce a structure-agnostic inverse-design workflow that turns heterogeneous literature and database evidence into experiment-prioritized shortlists of high-temperature high-entropy superalloy candidate chemistries. Unlike most data-driven high-entropy alloy (HEA) design studies that optimize a small set of proxies, we provide an end-to-end, reproducible decision-support loop that treats high-temperature creep and oxidation as first-class objectives and outputs compact shortlists for downstream validation. From curated multi-source data, we learn structure-agnostic, physics-informed surrogate models for the key high-temperature objectives – creep resistance (Larson–Miller parameter), oxidation kinetics (parabolic rate constant), melting point, density and elastic properties – and map predictions to “desirability” normalized scores. Candidate alloys are screened by a uniform feasibility floor and Pareto non-domination, then compressed by Ward–medoid clustering to yield compact, diversity-preserving shortlists for downstream validation. To explore beyond brute-force enumeration under the same admissibility rules, we couple the screening stage to a constraint-conditioned variational autoencoder, and retain only generated candidates that pass the full surrogate stack. The resulting compressed Pareto sets extend the occupied property envelope of legacy Ni-based superalloys while remaining interpretable through elemental-role analyses and Ashby-style trade-off maps. An external thermodynamic plausibility cross-check further shows that higher microstructure-oriented scores enrich the candidate space in single-phase HEA-compatible chemistries with wider stability windows and more favorable transition classes. Finally, we show how the same pipeline can be restricted to sustainability-compatible element pools, enabling performance-aware exploration under supply-risk and footprint constraints. high-entropy superalloys materials discovery inverse design computational materials visokoentropijske superzlitine odkrivanje materialov true false true Elsevier Nizozemska Angleški jezik Slovenski jezik © 2026 The Author(s). Neznano 2026-05-07 11:48:16 2026-05-07 11:48:16 2026-05-08 03:50:15 0000-00-00 00:00:00 2026 0 0 Nasl. z nasl. zaslona; Opis vira z dne 7. 5. 2026; str. [1-20] Vol. 266, [article no.] 116097 Jun. 2026 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2026-03-01 2026-04-22 2026-04-27 620.1/.2:004:8 1873-4197 10.1016/j.matdes.2026.116097 277335555 https://www.sciencedirect.com/science/article/pii/S0264127526006702?via%3Dihub 1 e076fdc4-49f9-11f1-8a5a-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=43884 RAZ_Rousseau_Francois_2026.pdf RAZ_Rousseau_Francois_2026.pdf 1 9A60D1BE93D85091530ED910B7D87A58 d16fee1aa5ebdf1d723064188ba60f187503e10d1ece70e3f871093a36222ebf 2439866b-49fa-11f1-8a5a-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=43885 Institut Jožef Stefan 0 0 0