Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuning
<|reference_start|>Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuning: 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.<|reference_end|>
@article{deng2024overcoming,
    title={Overcoming systematic softening in universal machine learning
  interatomic potentials by fine-tuning},
    author={Bowen Deng, Yunyeong Choi, Peichen Zhong, Janosh Riebesell, Shashwat
  Anand, Zhuohan Li, KyuJung Jun, Kristin A. Persson, Gerbrand Ceder},
    journal={arXiv preprint arXiv:2405.07105},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.07105},
    primaryClass={cond-mat.mtrl-sci cs.AI cs.LG}
}