Kavli Affiliate: Wei Gao
| First 5 Authors: Kai Liu, Kai Liu, , ,
| Summary:
Universal machine learning interatomic potentials (uMLIPs) are reshaping
atomistic simulation as foundation models, delivering near textitab initio
accuracy at a fraction of the cost. Yet the lack of reliable, general
uncertainty quantification limits their safe, wide-scale use. Here we introduce
a unified, scalable uncertainty metric (U) based on a heterogeneous model
ensemble with reuse of pretrained uMLIPs. Across chemically and structurally
diverse datasets, (U) shows a strong correlation with the true prediction
errors and provides a robust ranking of configuration-level risk. Leveraging
this metric, we propose an uncertainty-aware model distillation framework to
produce system-specific potentials: for W, an accuracy comparable to full-DFT
training is achieved using only (4%) of the DFT labels; for MoNbTaW, no
additional DFT calculations are required. Notably, by filtering numerical label
noise, the distilled models can, in some cases, surpass the accuracy of the DFT
reference labels. The uncertainty-aware approach offers a practical monitor of
uMLIP reliability in deployment, and guides data selection and fine-tuning
strategies, thereby advancing the construction and safe use of foundation
models and enabling cost-efficient development of accurate, system-specific
potentials.
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