The rational design of chemical catalysts requires methods for the measurement of free energy differences in the catalytic mechanism for any given catalyst Hamiltonian. The scope of experimental learning algorithms that can be applied to catalyst design would also be expanded by the availability of such methods. Methods for catalyst characterization typically either estimate apparent kinetic parameters that do not necessarily correspond to free energy differences in the catalytic mechanism or measure individual free energy differences that are not sufficient for establishing the relationship between the potential energy surface and catalytic activity. Moreover, in order to enhance the duty cycle of catalyst design, statistically efficient methods for the estimation of the complete set of free energy differences relevant to the catalytic activity based on high-throughput measurements are preferred. In this paper, we present a theoretical and algorithmic system identification framework for the optimal estimation of free energy differences in solution phase catalysts, with a focus on one- and two-substrate enzymes. This framework, which can be automated using programmable logic, prescribes a choice of feasible experimental measurements and manipulated input variables that identify the complete set of free energy differences relevant to the catalytic activity and minimize the uncertainty in these free energy estimates for each successive Hamiltonian design. The framework also employs decision-theoretic logic to determine when model reduction can be applied to improve the duty cycle of high-throughput catalyst design. Automation of the algorithm using fluidic control systems is proposed, and applications of the framework to the problem of enzyme design are discussed.