Dresden 2026 – wissenschaftliches Programm
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MM: Fachverband Metall- und Materialphysik
MM 21: Phase Transformations II
MM 21.5: Vortrag
Mittwoch, 11. März 2026, 11:15–11:30, SCH/A215
Ordering in Mo-Cr-Ti-Al refractory high-entropy alloys via machine learned interatomic potential — •Jiyao Zhang1, Klemens Lechner1, Markus Maßwohl2, Petra Spörk-Erdely2, and David Holec1 — 1Department of Materials Science, Montanuniversität Leoben, Franz-Josef-Strasse 18, 8700 Leoben, Austria — 2Institute of Materials Science, Joining and Forming, Graz University of Technology, Kopernikusgasse 24/I, 8010 Graz, Austria
Refractory high-entropy alloys (RHEAs) offer exceptional mechanical and thermal properties, such as excellent high-temperature strength, making them favorable competitors to nickel-based superalloys. Origin of the superior high-temperature performance of the Mo-Cr-Ti-Al RHEA can be tracked to its two-phase microstructure of disordered BCC and ordered B2 phases. Modeling RHEAs poses significant challenges, as accurate density functional theory (DFT) calculations are extremely time-consuming, a difficulty worsened by the alloys' complex compositions. Universal machine learning interatomic potentials (UMLIPs) have recently emerged as a promising solution. Trained on vast DFT datasets, UMLIPs enable near-DFT accuracy for large-scale simulations of thousands of atoms. In this work, we utilize UMLIPs in mixed Monte Carlo/Molecular Dynamics simulations to evaluate the thermal stability of ordered versus disordered states in Mo-Cr-Ti-Al system as a function of the Al contents. Furthermore, we analyze the impact of composition on order/disorder transition temperature and elastic properties. We validate our findings against experimental data.
Keywords: ordering; universal machine learned interatomic potentials; mechanical property