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.

翻译：不确定性量化（UQ）用于检测具有大预期误差的样本（异常值），并将其应用于反应性分子势能面（PESs）。三种方法——集成模型、深度证据回归（DER）和高斯混合模型（GMM）——被应用于${\it syn-}$克里吉中间体与乙烯基羟基过氧化物之间的氢转移反应。结果表明，集成模型在检测异常值方面效果最佳，其次是GMM。例如，在具有最大不确定性的1000个结构池中，如果寻找25个或1000个具有大误差的结构，则异常值的检测质量分别约为90%和50%。相反，DER统计假设的局限性极大地影响了其预测能力。最后，发现一种基于结构的指标与大平均误差相关，这可能有助于快速将新结构分类为那些对优化神经网络有益的结构。