Fig. 2: Results of UQ efficacy on the ammonia data set.

a Plots showing predicted uncertainties versus squared errors of atomic forces in the test data set for each considered UQ method trained only on the initial training data set (subfigure (c) below). The box at the top right corner of each subfigure shows the evaluation metrics of the methods, namely Spearman’s rank correlation coefficient (ρ), ROC AUC score (AUC), miscalibration area (Area), and calibrated negative log-likelihoods (cNLL). b Energy barrier of nitrogen inversion calculated with NEB using DFT and the considered UQ methods in generation 1. All NNs in the methods were trained on the same initial training data. c Histogram showing distribution of energy of geometries in the initial training data. d Fraction of stable MD trajectories generated using the NNs of the UQ methods in generation 1 as force field. e Energy barrier of nitrogen inversion calculated with NEB using DFT and the UQ methods in generation 3. The NNs are trained on new adversarial examples generated with their respective UQ method on top of the initial training data. f Distribution of energy in training set after two rounds of adversarial sampling is performed. The top and bottom horizontal marks denote the maximum and minimum values, respectively. g Fraction of stable MD trajectories generated using the NNs of the UQ methods in generation 3 as force field.