Various metrics have also been proposed to quantify the accuracy of explanation values. Some metrics focus on characterizing post hoc explanations, such as Grad-Cam 48 , non-zero reference , and the assumption of node explanation smoothness . Other metrics focus on the accuracy of the explanations with respect to the ground truth , which relies on the availability of ground-truth explanation datasets. The domain of XAI for molecular graphs is currently limited by the size of ground-truth datasets . Although there are several multimillion datasets of non-trivial molecular property values (e.g., QM9 , OC20 , and PCQM4Mv2 ) suitable for testing the prediction accuracy, no ground-truth explanation dataset of comparable size exists for testing the explanation accuracy. Some existing groundtruth datasets of atomistic explanations on molecular graphs explain trivial properties or are synthetic datasets . Other datasets are based on hundreds of hand-crafted structural subgraph motifs considered ground truth, for example, MUTAG and Ames mutagenicity . Further datasets consider other XAI methods as the reference, such as Crippen logP . This paper describes a general method to generate large datasets of accurate atomistic ground truth explanations for molecular graphs. Further, we apply this method to the HOMO-LUMO gap, generating more than 5 million ground truth explanations. The HOMO-LUMO gap is the energy difference between the Highest Occupied Molecular Orbital (HOMO) and Lowest Unoccupied Molecular Orbital (LUMO) for the ground state of a molecule. Accurate ab initio calculations of the HOMO-LUMO gap are time-consuming, despite being one of the most straightforward properties to calculate in quantum chemistry. The HOMO-LUMO gap can show long-range information propagation across the molecular graph, as exemplified in molecular wires, where the HOMO-LUMO gap shows a distance dependency up to 20 bonds . This dataset aims to fill the void of large datasets for explanation values on molecular graphs and is an opportunity to benchmark different XAI approaches and increase the accuracy and reliability of existing XAI methods. We anticipate the dataset will be useful for the resolution of the disagreement problem in explainable machine learning and for improving the reliability of explanations for chemical properties.