In addition, note that we always leave the points with the best and worst performances within each range outside of the training set. Therefore, the prediction of these points in the testing stage would be an extrapolation, instead of an interpolation in the design space. This approach validates the capability of our model to identify the best and worst performers outside of the training range, and this capability is crucial for formula screening.