  Machine learning interatomic potentials (MLIPs) have introduced a new
paradigm for atomic simulations. Recent advancements have seen the emergence of
universal MLIPs (uMLIPs) that are pre-trained on diverse materials datasets,
providing opportunities for both ready-to-use universal force fields and robust
foundations for downstream machine learning refinements. However, their
performance in extrapolating to out-of-distribution complex atomic environments
remains unclear. In this study, we highlight a consistent potential energy
surface (PES) softening effect in three uMLIPs: M3GNet, CHGNet, and MACE-MP-0,
which is characterized by energy and force under-prediction in a series of
atomic-modeling benchmarks including surfaces, defects, solid-solution
energetics, phonon vibration modes, ion migration barriers, and general
high-energy states.
  We find that the PES softening behavior originates from a systematic
underprediction error of the PES curvature, which derives from the biased
sampling of near-equilibrium atomic arrangements in uMLIP pre-training
datasets. We demonstrate that the PES softening issue can be effectively
rectified by fine-tuning with a single additional data point. Our findings
suggest that a considerable fraction of uMLIP errors are highly systematic, and
can therefore be efficiently corrected. This result rationalizes the
data-efficient fine-tuning performance boost commonly observed with
foundational MLIPs. We argue for the importance of a comprehensive materials
dataset with improved PES sampling for next-generation foundational MLIPs.
