A second opportunity for applying ML is to increase accuracy in traditional workflows. As described previously, standard computational screening workflows often skip explicit DFT calculations in favor of simplifications such as scaling relationships to model adsorption energies and BEP relationships to model reaction kinetics. Unlike DFT calculations, these simplifications enforce rigid relationships between simple descriptors and more complex properties, making it difficult to identify the subset of materials that do not follow the usual trends. Indeed, materials whose behavior is not adequately described by fitted descriptor relationships may often be the most interesting for catalysis. Machine learning models may make it possible to remove the use of scaling relationships altogether, replacing them with ML predictions that better model the complex behavior of catalyst materials.