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.
What do we learn? Debunking the Myth of Unsupervised Outlier Detection
Technical University of Munich（慕尼黑工业大学）；Helmholtz Center Munich（亥姆霍兹慕尼黑中心）；Helmholtz AI（亥姆霍兹人工智能研究所）；Klinikum Rechts der Isar（伊萨尔河右岸医院）；Imperial College London（帝国理工学院）；King’s College London（伦敦国王学院）
Machine-learned exchange-correlation functionals for molecules, solids, and reactive surfaces
Harvard University（哈佛大学）；Massachusetts Institute of Technology（麻省理工学院）；Robert Bosch LLC Research and Technology Center（罗伯特博世有限责任公司研究与技术中心）；Initiative for Computational Catalysis, Flatiron Institute（计算催化倡议，平顿研究所）
Teachers that teach the irrelevant: Pre-training machine learned interaction potentials with classical force fields for robust molecular dynamics simulations
University of California, Berkeley（加州大学伯克利分校）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）
Enerzyme: A Framework for Efficient Training of Reactive Neural Network Potentials for Enzyme Catalysis with Application to Methyltransferases
Department of Chemical Engineering, Massachusetts Institute of Technology（麻省理工学院化学工程系）；Department of Chemistry, Massachusetts Institute of Technology（麻省理工学院化学系）
Machine Learning Product State Distributions from Initial Reactant States for a Reactive Atom-Diatom Collision System
University of Basel（巴塞尔大学）；Indian Institute of Science Education and Research (IISER) Tirupati（印度科学教育与研究学院蒂鲁帕蒂分院）；Stanford University（斯坦福大学）；Air Force Research Laboratory（美国空军研究实验室）
Molecular Simulation for Atmospheric Reaction Exploration and Discovery: Non-Equilibrium Dynamics, Roaming and Glycolaldehyde Formation Following Photo-Induced Decomposition of syn-Acetaldehyde Oxide
Smite: A quasiclassical trajectory (QCT) program for bimolecular collisions and unimolecular dynamics on ab-initio, and machine-learned potential energy surfaces
Belgian Institute for Space Aeronomy (BIRA-IASB)（比利时空间气旋研究所）；Department of Chemistry, KU Leuven（荷语鲁汶大学化学系）
What Rate Theories Compute: Chemical kinetics from Arrhenius to machine-learned potentials
Center for Multidimensional Carbon Materials, Institute for Basic Science (IBS)（多维碳材料中心，韩国基础科学研究所）；Department of Chemistry, Ulsan National Institute of Science and Technology (UNIST)（蔚山科学技术院化学系）
