Researchers have developed AdsorbML, a machine learning approach that improves efficiency in adsorption energy calculations. The method utilizes generalizable machine learning potentials and has been explored in various studies, including those focused on predicting global minimum adsorption energy and designing catalytic active sites. These developments are part of a broader effort to advance catalyst discovery and surface reaction understanding, with some studies also involving the creation of open databases and frameworks for determining adsorption energies.