Flexible Cutoff Learning: Optimizing Machine Learning Potentials After Training
We introduce Flexible Cutoff Learning (FCL), a method for training machine learning interatomic potentials (MLIPs) whose cutoff radii can be adjusted after training. Unlike conventional MLIPs that fix the cutoff radius during training, FCL models are trained by randomly sampling cutoff radii independently for each atom. The resulting model can then be deployed with different per-atom cutoff radii depending on the application, enabling application-specific optimization of the accuracy-cost tradeoff. Using a differentiable cost model, these per-atom cutoffs can be optimized for specific target systems after training. We demonstrate FCL with a modified MACE architecture trained on the MAD da...
| Authors | Rick Oerder, Jan Hamaekers |
|---|---|
| Primary category | cond-mat.mtrl-sci |
| Published | 10 March 2026 |
| Last updated at source | 10 March 2026 |
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