| Title: | Inverse design of high-entropy superalloys using machine learning and generative artificial Intelligence |
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| Authors: | ID Rousseau, François (Author)
ID Belmonte, Thierry (Author)
ID Sur, Frédéric (Author)
ID Nominé, Alexandre, Institut "Jožef Stefan" (Author) |
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| Files: |  URL - Source URL, visit https://www.sciencedirect.com/science/article/pii/S0264127526006702?via%3Dihub
 PDF - Presentation file, download (11,96 MB)
MD5: 9A60D1BE93D85091530ED910B7D87A58
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| Language: | English |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: |  IJS - Jožef Stefan Institute
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| Abstract: | 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. |
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| Keywords: | high-entropy superalloys, materials discovery, inverse design, computational materials |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 01.03.2026 |
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| Article acceptance date: | 22.04.2026 |
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| Publication date: | 27.04.2026 |
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| Publisher: | Elsevier |
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| Year of publishing: | 2026 |
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| Number of pages: | str. [1-20] |
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| Numbering: | Vol. 266, [article no.] 116097 |
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| Source: | Nizozemska |
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| PID: | 20.500.12556/DiRROS-29321  |
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| UDC: | 620.1/.2:004:8 |
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| ISSN on article: | 1873-4197 |
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| DOI: | 10.1016/j.matdes.2026.116097 |
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| COBISS.SI-ID: | 277335555 |
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| Copyright: | © 2026 The Author(s). |
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| Note: | Nasl. z nasl. zaslona;
Opis vira z dne 7. 5. 2026;
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| Publication date in DiRROS: | 07.05.2026 |
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| Views: | 32 |
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| Downloads: | 17 |
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