where ÊRPBE are the aligned energies of the RPBE functional to the BEEF-vdW functional. Eq. ( ) allows to align the data from various sources to the same QM zero-of-energy, which is useful in data science and machine learning for providing an unbiased error between different levels of theory . The method is mathematically similar to the ∆-ML approach, where the thermochemical quantity from on level of theory can be mapped onto another level of theory . Here, we present how to do this using least-squares regression to determine elemental chemical potentials. However, the least-squares regression can be replaced with a machine learning model to regress other structural features using e.g. a LASSO (least absolute shrinkage and selection operator) optimization . The downside of this approach is that the anchor chemical potentials will depend implicitly on all the species present in the dataset and technically need to be re-computed any time the network is expanded, similar to the ATcT .