arXiv2026
Machine learning interatomic potentials (MLIPs) bridge the gap between quantum-mechanical accuracy and scalable molecular simulations. However, a fundamental trade-off persists: universal MLIPs (u-MLIPs) offer broad transferability but are computationally expensive, whereas task-specific MLIPs (ts-MLIPs) achieve high efficiency at the expense of costly DFT training data. Here we introduce LightPFP, a data-efficient knowledge distillation framework. Instead of relying on expensive DFT calculations, LightPFP constructs a distilled ts-MLIP by using a u-MLIP to generate high-quality, material-specific training data, while employing a pre-trained lightweight model to further improve data efficiency. Across diverse material systems, LightPFP reduces model development time by approximately three orders of magnitude compared with DFT-based workflows, while maintaining DFT-level accuracy. Combined with a LAMMPS/Kokkos inference backend, LightPFP achieves more than three orders of magnitude higher inference efficiency than u-MLIP baselines while enabling simulations of systems thousands of times larger. When the teacher exhibits systematic bias, LightPFP supports few-shot precision correction using as few as 10 high-accuracy DFT configurations, as demonstrated by reproducing MgO's experimental melting point. This u-MLIP-driven distillation establishes a scalable route to high-fidelity, data-efficient MLIPs, accelerating the pace of materials discovery and design.