Microkinetic models of homogeneous catalytic reactions are constructed using ab initio simulations to gain insight into mechanisms, rationalize catalyst performance, and inspire catalyst design.Using a data-driven approach, the classical linear free-energy relationships are extended to multiple linear regression to study both reactivity and selectivity of homogeneous catalysts and build models to rationalize important interactions.Volcano plots are demonstrated as a general analysis framework when comparing potential energy surfaces of related catalytic reactions to extract decisive parameters and visualize breaks in mechanisms.Nonlinear regression models including random forests, support-vector machines, Gaussian processes, and artificial neural networks are applied to predict reaction yields, enantioselectivity, and activation energies of catalytic reactions based on both experimental and computational data.