Each configuration is represented by a 6dimensional vector of the descriptor values, which is shown in Table of the SI. In order to evaluate the performance of the ML learning models, the data set is randomly split into a training data set (containing 90% of TS config-urations) and a test set (containing the remaining 10% of the configurations); the test set in each trial never overlaps with the model set in that trial. The most important metric to evaluate the performance of the fitting is the root mean square error (RMSE) with respect to the DFT values (the ground truth), but we also report the R 2 score when relevant. Initially, we tested two ML models: the linear ordinary least squares regression (OLS) and the non-linear random forest regression (RF). The linear regression model should give a good performance for a small data set. On the other hand, the non-linear RF model (based on an ensemble of decision trees regression) is more flexible than a linear one but it typically requires larger data sets. However, RF consistently outperformed OLS, as shown in Figure of the SI for a single-shot random trial; thus, we abandoned the OLS model and we continue the discussion only with the RF model.