However, to realize the accuracy gains, it is well known that energy corrections need to be applied to the QM calculations to reduce residual errors present from the given level of theory (LOT, a.k.a. model chemistry). Usually, atomization energy corrections (AECs) are needed to correct for the large systematic errors in the calculated absolute energies of the atoms, and to connect the computed zero of energy to the standard state zero of energy used when defining the enthalpy of formation (a.k.a. heats of formation, formation enthalpy) ∆H f . As we show later in this work, if no additional corrections are used, the resulting ∆H f tends to have a fairly wide distribution of errors, often not centered around zero. The need for additional corrections is well known, and in fact, such corrections have already been derived for some DLPNO methods. There are several approaches to further improve the accuracy of QM thermochemistry beyond the corrections provided by AECs. Recently, delta machine learning (∆-ML) techniques have been applied to learn thermochemistry corrections . However, while ML methods for thermochemistry prediction have gained popularity, they often suffer from a lack of interpretability compared to classic strategies, making it more challenging to understand how the models arrive at their results and to identify potential sources of error.