Researchers are exploring the use of machine learning to improve predictions of adsorption energy, with some studies focusing on the density of states and others on related concepts such as correlation density functionals and electron density. The application of machine learning in this area may have implications for fields like catalysis. Various approaches are being developed, including the use of hybrid density functionals and one-electron reduced density matrices, to enhance the accuracy of these predictions.