Data-efficient adaptation of DPA-4 force fields to DFT+U energetics: a case study in NiO

Foundation machine-learned force fields (MLFFs) are often pretrained on broad materials datasets whose electronic-structure conventions may not reproduce the phase energetics required for a specific correlated material. Using NiO as a case study, we examine whether distinct source-protocol energetics can be adapted efficiently through target-protocol fine-tuning. Along a common fixed structural interpolation, non-spin-polarized Perdew–Burke–Ernzerhof (PBE) and ferromagnetically initialized PBE+U predict opposite energetic orderings of the octahedral Oct and square-planar Sqr phases. When fine-tuned directly, the pretrained DPA-4 models adapt rapidly to the NiO PBE+U surface, reaching energy, force, and virial root-mean-square errors (RMSEs) of approximately 0.4-0.5 meV/atom, 30-35 meV/Å, and 5-6 meV/atom, respectively, with 170 PBE+U training configurations. Crucially, models previously fine-tuned to the opposing no-U surface recover the qualitative target-protocol energetic ordering along the fixed structural path with nearly the same target-label efficiency as models fine-tuned directly from their respective pretrained initializations. Our results show that distinct source-protocol energetics can be reversed through target-protocol fine-tuning, and suggest a practical multi-fidelity strategy in which pretraining prioritizes broad, consistent, and affordable data, while compact target-protocol datasets impose energetics through application-specific fine-tuning.