Constructing machine learning interatomic potentials with minimum amount of ab initio data

Machine learning interatomic potentials (MLIP) are powerful tools for using large-scale molecular dynamics (MD) to evaluate material properties, including the performance of solid-state electrolytes (SSEs). While there are many efforts for constructing universal big MLIP models, their accuracies and speeds of inference still need to be improved for many practical applications. Another approach is to develop a system-specific MLIP model relying on active learning strategy. Although much cheaper than training a big model, using the conventional procedure, it still requires large numbers of active learning loops and the corresponding DFT calculations to ensure convergency. Here, we propose a single-shot workflow that significantly accelerates small MLIP model development by leveraging the capabilities of the big model (using MACE as one example) and requiring only a few hundred additional DFT calculations. Our workflow comprises two stages, first the MACE model itself is fine-tuned to make it more accurate for the given system, second a smaller MLIP model (using NEP as one example) is distilled from the fine-tuned MACE model. We employed a MACE-driven sampling strategy, carried out additional DFT calculations without relying on active learning iterations. We show that fine-tuned MACE model can inherit the stability of the pretrained model, and fine-tuning the pretrained MACE model is much more DFT data efficient comparing to training a start-from-scratch NEP model. In the second stage, the fine-tuned MACE model provides the dataset to train the NEP model, allows the final NEP model to carry out large scale MD simulations with competitive accuracy. This integrated workflow establishes a systematic pathway for rapid MLIP development via small additional DFT dataset, with potential applications to many material systems.