In addition, we train a random forest regression model with 200 estimators as implemented in scikit-learn. By accessing the trained estimators in the random forest model, we can obtain a target prediction for all 200 decision trees and calculate a standard deviation. This standard deviation is then used to estimate the prediction uncertainties of the final LightGBM model and to forecast out-of-sample data points where running the QM-based workflow would be recommended. The performance of this uncertainty estimation approach is evaluated using error-based calibration following the work of Rasmussen et al. Errorbased calibration is an uncertainty quantification (UQ) metric proposed by Levi et al. based on the principle that the root mean square error (RMSE) should directly correlate with the root mean variance (RMV). To explore the local relationship between the predicted uncertainties (σ) and errors (ϵ = y pred -y true ) of the ML predictions (y pred ) compared to the QM calculations (y true ), the two properties are sorted and binned according to the uncertainty. For each bin containing N bin samples, RMSE and RMV are calculated as follows: