Beyond structure-based models. While the above results show that λ DFT can be learned from the structure, the accuracy of the models leaves something to be desired, given that the intrinsic standard deviation of the dataset is ca. 80 meV. This underscores our previous point on the highly challenging nature of λ as a target property, e.g. compared to the atomization energy. Furthermore, robust models require the use of GFN1-xTB for conformer ranking. It is therefore natural to ask whether electronic properties at the GFN1-xTB level could be used to improve these models. The most straightforward way to do this is via a ∆-learning 37 strategy, i.e. by learning a correction to λ GFN1 . To this end, we first use a simple linear regression to describe systematic differences between λ DFT and λ GFN1 :