Machine learning interatomic potentials for solid-state precipitation

Machine learning interatomic potentials (MLIPs) are routinely used to model diverse atomistic phenomena, yet parameterizing them to accurately capture solid-state phase transformations remains difficult. We present error metrics and data-generation schemes designed to streamline the parameterization of MLIPs for modeling precipitation in multicomponent alloys. We developed an algorithm that enumerates symmetrically distinct transformation pathways connecting chemical decorations of different parent crystal structures. Additionally, we introduce the weighted Kendall- τ coefficient and its semigrand-canonical generalization as metrics for quantifying MLIP accuracy in predicting low-temperature thermodynamics. We apply these approaches to parameterize an MLIP for a dilute Mg–Nd alloy. The resulting potential reproduces the complex early stage precipitation behavior observed experimentally. Large-scale atomistic simulations reveal competition between order-disorder and structural transformations. Furthermore, these results suggest a continuous transition between high-symmetry hcp and bcc crystal structures during aging heat treatments.