1. Percentage of χnew = 0, percentage of χnew = 1, percentage of χnew ≤ 0.1, percentage of χnew ≤ 0.2 and percentage of χnew ≤ 0.5 of the entire and the last 40% of the given χnew vs ropt plot 2. The highest ropt value at which χnew = 1 3. ropt value of the last data point in the plot 4. Total number of data points in the plot The χnew vs ropt plot pattern depends on the order of the conformers in the priority list. Using priority lists generated under a specific setting, 150 molecules within the DFT dataset are expected to yield 15342 descriptor arrays. We have considered nine different priority list generation approaches to generate as many χnew vs ropt plots as possible for building up the training data: pipeline-x (x = 0.8), pipeline-ascent, three sets of pipeline-mix (x = 0.8; Q = 0.15, 0.20, 0.25) and four sets of every n th (n = 1-4) priority list. This leads to 15342×9 = 138078 sets of a descriptor array and label. Given a list of χnew and ropt values for a partial re-optimization, should the optimization continue? We have the answer for the 138078 examples from exhaustive reoptimization. Can this training set be used to generate a reliable guide to how many structures in a new conformation search need to be re-optimized? We evaluated various classification methods from scikitlearn : random forest (RF), K-nearest neighbor classification (KNN), logistic regression (LR) and Gaussian Naive Bayes (GaussianNB). To assess the performance of the models in predicting the completion of the re-optimization process, we followed a rigorous cross-validation methodology. For each molecule, we took out the relevant data points associated with the molecule, which corresponds to less than 1% of the entire dataset. Models were trained on the filtered dataset. Input descriptor arrays derived from the default CONFPASS priority list generation approach (pipeline-mix with x = 0.8 and Q = 0.2) of the molecule were used for testing. Each molecule contributes m sets of prediction results from the designated model, where m is the total number of conformers. The above processes gave 15342 prediction results for each classification method. The default hyperparameter settings were used. The LR model gives the highest accuracy, 0.88. The GaussianNB model has an accuracy of 0.86. KNN and RF model show similar performance with an accuracy of around 0.81. We also built a local model from χnew vs ropt plots based on the pipeline-mix priority list (x = 0.8 and Q = 0.2) by itself. The mean accuracy of the local model was very similar to the global model. its confidence in each classification. Figure presents a plot of p LR vs ropt from the cross-validation test of the global LR model. More than 50% of the false predictions have a ropt value of less than 0.2. The outcome is within the expectation because the predictions rely on a descriptor array derived from a relatively limited amount of data. Using the results from the cross-validation test, we found that the p LR correlates with the percentage of true predictions in our 150 molecules dataset (%Conf; ie percentage confidence to terminate the re-optimization process) via a sigmoid curve (eq.3; SI Section 7.C). The mean absolute error of the fit was 2.6%. %Conf = ' '() !".$%(' () !*.+)