In 2015, Ramakrishnan et al. introduced the concept of ∆-ML models to chemical applications. These models are trained to infer the difference in value of a property computed by two quantum chemical methods. Usually, a lower-quality method is used as base method upon which the high-level method value is predicted. Regarding the base method, GFN2-xTB is a popular choice due to the factors summarized previously in this work. Data-driven models are also compared against xTB reference values.