Foundational Machine‐Learning Interatomic Potential for Simulating Chemically Complex Ni‐Based Superalloys

Abstract

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 γ$\gamma$ and γ′$\gamma &aposx;$ 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%$0.50\%$ of DFT references, while underestimating melting temperatures of ordered Ni–Al phases by up to 5.7%$5.7\%$ . The simulations reveal local chemical ordering in the γ$\gamma$ phase and the expected L12$\mathrm{L1_{2}}$ sublattice occupancies in the γ′$\gamma &aposx;$ phase. In the γ$\gamma$ phase, the short‐range order raises the shear barriers by approximately 66 mJ m−2$66~\mathrm{mJ\,m^{-2}}$ while leaving the intrinsic stacking fault energy of 28 mJ m−2$28~\mathrm{mJ\,m^{-2}}$ unchanged. In the γ′$\gamma &aposx;$ phase, alloying raises the complex and superlattice intrinsic stacking fault energies by approximately 100 mJ m−2$100~\mathrm{mJ\,m^{-2}}$ relative to stoichiometric Ni3$\mathrm{Ni_{3}}$ Al. These results show that pretrained foundational potentials enable atomistic simulations of chemically complex multicomponent superalloys at scales inaccessible to direct first‐principles calculations.