Just another WordPress site - Ruhr-Universität Bochum

For decades, atomistic simulation of chemically complex Ni-based superalloys has remained beyond practical reach. Here, we apply the GRACE foundational machine-learning interatomic potential to predict chemical ordering and stacking-fault energetics in the $\gamma$ and $\gamma$′ phases of CMSX-4, a commercial multicomponent Ni-based superalloy. After benchmarking against structural and thermodynamic reference data, we use hybrid Monte-Carlo/molecular dynamics sampling to study the impact of local chemical order on planar-fault energies. GRACE reproduces elemental equilibrium lattice parameters within 0.50\% of DFT references, while underestimating melting temperatures of ordered Ni–Al phases by up to 5.7\% . The simulations reveal local chemical ordering in the $\gamma$ phase and the expected $L⁢1_2$ sublattice occupancies in the $\gamma$′ phase. In the $\gamma$ phase, the short-range order raises the shear barriers by approximately 66 mJ m$^{−2}$ while leaving the intrinsic stacking fault energy of 28 mJ m$^{−2}$ unchanged. In the $\gamma$′ phase, alloying raises the complex and superlattice intrinsic stacking fault energies by approximately 100 mJ m$^{−2}$ relative to stoichiometric Ni$_3$Al. These results show that pretrained foundational potentials enable atomistic simulations of chemically complex multicomponent superalloys at scales inaccessible to direct first-principles calculations.