Training and use of an NN model for the LMF ("n-LMF") are done in two separate phases. The training of the LMF is done in a post-SCF manner in a locally developed python code connected to the TensorFlow library. The input features of the LMF are transferred from the Turbomole code and are based on a human-designed functional, ensuring that we have well-defined descriptors. The weights of the trained n-LMF are subsequently transferred back to Turbomole, where standard self-consistent DFT calculations with the n-LMF can then be conducted.