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 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 ∼90 \% and ∼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.
@article{arxiv.2402.17686,
title = {Outlier-Detection for Reactive Machine Learned Potential Energy Surfaces},
author = {Luis Itza Vazquez-Salazar and Silvan Käser and Markus Meuwly},
journal= {arXiv preprint arXiv:2402.17686},
year = {2024}
}