Outlier-Detection for Reactive Machine Learned Potential Energy Surfaces
<|reference_start|>Outlier-Detection for Reactive Machine Learned Potential Energy Surfaces: 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.<|reference_end|>
@article{vazquez-salazar2024outlier-detection,
    title={Outlier-Detection for Reactive Machine Learned Potential Energy Surfaces},
    author={Luis Itza Vazquez-Salazar and Silvan K"aser and Markus Meuwly},
    journal={arXiv preprint arXiv:2402.17686},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.17686},
    primaryClass={physics.chem-ph cs.LG}
}