Szczegóły publikacji

Opis bibliograficzny

Uncertainty-aware design of high-entropy alloys via ensemble thermodynamic modeling and search space pruning / Roman DĘBSKI, Władysław Gąsior, Wojciech Gierlotka, Adam Dębski // Applied Sciences (Basel) [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2076-3417. — 2025 — vol. 15 iss. 16 art. no. 8991, s. 1–18. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 17–18, Abstr. — Publikacja dostępna online od: 2025-08-14

Autorzy (4)

Słowa kluczowe

Monte Carlo simulationcomputational alloy designthermodynamic modelingHEAsearch space pruninguncertainty quantificationhigh entropy alloys

Dane bibliometryczne

ID BaDAP162137
Data dodania do BaDAP2025-09-10
Tekst źródłowyURL
DOI10.3390/app15168991
Rok publikacji2025
Typ publikacjiartykuł w czasopiśmie
Otwarty dostęptak
Creative Commons
Czasopismo/seriaApplied Sciences (Basel)

Abstract

The discovery and design of high-entropy alloys (HEAs) faces significant challenges due to the vast combinatorial design space and uncertainties in thermodynamic data. This work presents a modular, uncertainty-aware computational framework with the primary objective of accelerating the discovery of solid-solution HEA candidates. The proposed pipeline integrates ensemble thermodynamic modeling, Monte Carlo-based estimation, and a structured three-phase pruning algorithm for efficient search space reduction. Key quantitative results are achieved in two main areas. First, for binary alloy thermodynamics, a Bayesian Neural Network (BNN) ensemble trained on domain-informed features predicts mixing enthalpies with high accuracy, yielding a mean absolute error (MAE) of 0.48 kJ/mol-substantially outperforming the classical Miedema model (MAE = 4.27 kJ/mol). These probabilistic predictions are propagated through Monte Carlo sampling to estimate multi-component thermodynamic descriptors, including Delta Hmix and the Omega parameter, while capturing predictive uncertainty. Second, in a case study on the Al-Cu-Fe-Ni-Ti system, the framework reduces a 2.4 million (2.4 M) candidate pool to just 91 high-confidence compositions. Final selection is guided by an uncertainty-aware viability metric, P(HEA), and supported by interpretable radar plot visualizations for multi-objective assessment. The results demonstrate the framework's ability to combine physical priors, probabilistic modeling, and design heuristics into a data-efficient and interpretable pipeline for materials discovery. This establishes a foundation for future HEA optimization, dataset refinement, and adaptive experimental design under uncertainty.