Structure-function relationships are leveraged to provide mechanistic insight into the connections between catalyst structural features and observed experimental outcome. A diverse array of steric molecular descriptors has historically captured structural features of 3D-representations for application to statistical modeling and machine learning (ML) prediction of reaction performance. Traditional steric descriptors, including Sterimol 1,2 (L, B1, B5) and buried volume (VBur), are successfully applied to diverse areas of catalysis and provide unique insight into structure-function relationships from resultant ML models. However, limitations of many steric molecular descriptors prevent their application to certain complex systems.