Foundational Machine‐Learning Interatomic Potential for Simulating Chemically Complex Ni‐Based Superalloys
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 and 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 of DFT references, while underestimating melting temperatures of ordered Ni–Al phases by up to . The simulations reveal local chemical ordering in the phase and the expected sublattice occupancies in the phase. In the phase, the short‐range order raises the shear barriers by approximately while leaving the intrinsic stacking fault energy of unchanged. In the phase, alloying raises the complex and superlattice intrinsic stacking fault energies by approximately relative to stoichiometric Al. These results show that pretrained foundational potentials enable atomistic simulations of chemically complex multicomponent superalloys at scales inaccessible to direct first‐principles calculations.