Outlier-Detection for Reactive Machine Learned Potential Energy Surfaces

arXiv:2402.17686v1 Announce Type: cross
Abstract: 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 …

abstract arxiv cs.lg detection energy errors machine outlier outlier-detection outliers physics.chem-ph quantification regression samples syn transfer type uncertainty