Weights from OMat24 force errors turn an eleven-model heterogeneous uMLIP ensemble into an uncertainty metric U that correlates with true force errors across material families and drives low-DFT distillation.
Riebesell, et al., A framework to evaluate machine learning crystal stability predictions
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Heterogeneous Ensemble Enables a Universal Uncertainty Metric for Atomistic Foundation Models
Weights from OMat24 force errors turn an eleven-model heterogeneous uMLIP ensemble into an uncertainty metric U that correlates with true force errors across material families and drives low-DFT distillation.