Foundation model distillation with PiNNAcLe: Application to the ZnCl2 aqueous electrolyte solution

Aqueous zinc chloride solutions are central to zinc-based electrochemical energy storage and electrochemical sensors, yet their local structure and dynamics remain challenging to characterize without atomistic simulation. Foundation models for materials chemistry can substantially accelerate such simulations, but they often impose significantly higher computational overhead than bespoke machine-learning interatomic potential. Here we bridge these two regimes by developing a workflow that distils a foundation model into our lightweight PiNet2 architecture, enabled by an extension of PiNNAcLe (Pair-wise Interaction Neural Network with Active Learn-on-the-fly). We demonstrate the approach for ZnCl 2 (aq), and show that the framework can seamlessly switch between two foundation models at different density functional theory levels. Structural and dynamical properties from the distilled models have been validated against the foundation models as well as the density functional theory-based molecular dynamics simulations at matched levels of theory. This work provides a model-agnostic route to harnessing foundation models with low-cost bespoke models, enabling routine, scalable simulations of electrolyte systems.