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Stable Machine Learning Potentials for Liquid Metals via Dataset Engineering

Liquid metals are central to energy-storage and nuclear technologies, yet quantitative knowledge of their thermophysical properties remains limited. While atomistic simulations offer a route to computing liquid properties directly from atomic motion, the most accurate approach, ab initio molecular dynamics (AIMD), is computationally costly and restricted to short time and length scales. Machine learning interatomic potentials (MLPs) offer AIMD accuracy at far lower cost, but their application to liquids is limited by training datasets that inadequately sample atomic configurations, leading to unphysical force predictions and unstable trajectories. Here we introduce a physically motivated dataset-engineering strategy that constructs liquidlike training data synthetically rather than relying on AIMD configurations. The method exploits the established icosahedral short-range order of metallic liquids, twelvefold, near-close-packed local coordination, and generates "synthetic-liquid" structures by systematic perturbation of crystalline references. MLPs trained on these datasets close the sampling gaps that lead to unphysical predictions, remain numerically stable across temperatures, and reproduce experimental liquid densities, diffusivities, and melting temperatures for multiple elemental metals. The framework links atomic-scale sampling to long-term MD stability and provides a practical route to predictive modeling of liquid-phase thermophysical behavior beyond the limits of direct AIMD.