Outlier-Detection for Reactive Machine Learned Potential Energy Surfaces
Luis Itza Vazquez-Salazar Silvan Käser Markus Meuwly
Uncertainty quantification (UQ) to detect samples with large expected errors
(outliers) is applied to reactive molecular potential energy surfaces (PESs).
Three methods - Ensembles, Deep Evidential Regression (DER), and Gaussian
Mixture Models (GMM) - were applied to the H-transfer reaction between ${\it
syn-}$Criegee and vinyl hydroxyperoxide. The results indicate that ensemble
models provide the best results for detecting outliers, followed by GMM. For
example, from a pool of 1000 structures with the largest uncertainty, the
detection quality for outliers is $\sim 90$ \% and $\sim 50$ \%, respectively,
if 25 or 1000 structures with large errors are sought. On the contrary, the
limitations of the statistical assumptions of DER greatly impacted its
prediction capabilities. Finally, a structure-based indicator was found to be
correlated with large average error, which may help to rapidly classify new
structures into those that provide an advantage for refining the neural
network.