Recognizing the potential of ∆-ML models, the MultiXC-QM9 127 dataset was curated by Nandi et al. It encompasses the reaction energies for 162 million reactions with 228 quantum chemical methods, including GFN2-xTB energies. This allows to build elaborate ∆-ML models for small organic molecules. ∆-learning approaches have also been published in combination with ML-potentials. Staub et al. combined the SpookyNet 128 framework with GFN2-xTB as a base level. This approach proved to be superior in the prediction of chemical yields for Wilkinson's catalyst, compared to only relying on GFN2-xTB calculations. A similar approach for building potential for molecular dynamics simulations is published by In their work, a polynomial fit is used instead of neural network models.