Dataset distillation for machine learning force field in phase transition regime

Machine learning force fields (MLFFs) have emerged as powerful data-driven tools for atomistic simulations, enabling large-scale and complex atomic systems to be simulated with accuracy comparable to that of ab initio methods. However, MLFFs often suffer from low training efficiency in the phase transition regime, where structural fluctuations are significantly elevated. To address this challenge, we propose a Central-Peripheral Distillation (CPD) algorithm for training dataset distillation. By strategically integrating representative samples with critical corner cases, the CPD algorithm ensures that the distilled dataset retains maximum structural diversity. We validated the efficacy of the CPD method on the liquid-liquid phase transition of dense hydrogen. Results show that, with the CPD approach, only 150 configurations are sufficient to train a MLFF that can fully reproduce the structural and dynamical properties of liquid hydrogen in the vicinity of its phase transition regime. This work paves the way for high-fidelity labeling of the MLFF training datasets, for instance, by adopting high-level ab initio calculations beyond the standard density functional theory, thereby enhancing the predictive accuracy of MLFFs.