Regensburg 2025 – scientific programme

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MM: Fachverband Metall- und Materialphysik

MM 18: SYMD contributed

MM 18.8: Talk

Wednesday, March 19, 2025, 12:15–12:30, H23

Data driven prediction of relative stability of binary and ternary TCP phases. — •Mariano Forti, Ralf Drautz, and Thomas Hammerschmidt — Interdisciplinary centre for advanced materials simulation, Ruhr-University Bochum

The study of precipitation of topological close packed(TCP) phases is of primary importance for the performance of superalloys. However, the structural complexity of these intermetallic compounds and the chemical complexity of the superalloys with typically up to ten elements hampers the exhaustive sampling of chemical space by density-functional theory (DFT) calculations. We overcome the related computational limitations by combining machine learning (ML) techniques with descriptors of the local atomic environment of the TCP phases and the use of interatomic potentials to predict phase properties with high precision. We illustrate our methodology studying the relative stability of the complex phases R, P, M and δ in binary and ternary systems produced from the main components in Co, Ni and Fe based superalloys.

Keywords: TCP phases; Machine learning; Formation enthalpies; Superalloys