Dataset and text of the master thesis "Exploring Symbolic Regression for hypothesis testing of London-Dispersion corrections in theoretical molecular physics" by Gerfried Millner. The Equation Learner Version used in this thesis is available at: https://github.com/GMillner/eql-etc Abstract: "In quantum chemistry, material science, physics and other fields, modeling atoms and molec- ular systems is becoming increasingly popular over the last decades. Approaches, like the Hartree-Fock method (HF), do not include the total electronic energy as compared to more advanced ones (e.g. Coupled Cluster), which are computationally much more demanding and therefore several orders of magnitudes slower to simulate the required task. The difference of HF and post-HF methods is improved by adding the London-dispersion interaction, an attrac- tive van der Waals force. While its principle dependence on interatomic distance is well known, several improvements have been suggested in the past. In this work interpretable correlations for this correction are searched using a machine learning method called Symbolic Regression and the data input of atomic pairs moving apart from each other"