Kavli Affiliate: Wei Gao
| First 5 Authors: Fei Shuang, Fei Shuang, , ,
| Summary:
Machine learning interatomic potentials (MLIPs) enable accurate atomistic
modelling, but reliable uncertainty quantification (UQ) remains elusive. In
this study, we investigate two UQ strategies, ensemble learning and
D-optimality, within the atomic cluster expansion framework. It is revealed
that higher model accuracy strengthens the correlation between predicted
uncertainties and actual errors and improves novelty detection, with
D-optimality yielding more conservative estimates. Both methods deliver well
calibrated uncertainties on homogeneous training sets, yet they underpredict
errors and exhibit reduced novelty sensitivity on heterogeneous datasets. To
address this limitation, we introduce clustering-enhanced local D-optimality,
which partitions configuration space into clusters during training and applies
D-optimality within each cluster. This approach substantially improves the
detection of novel atomic environments in heterogeneous datasets. Our findings
clarify the roles of model fidelity and data heterogeneity in UQ performance
and provide a practical route to robust active learning and adaptive sampling
strategies for MLIP development.
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