Lattice kinetic Monte Carlo simulations have become a vital tool for
predictive quality atomistic understanding of complex surface chemical
reaction kinetics over a wide range of reaction conditions. In order to expand
their practical value in terms of giving guidelines for the atomic level
design of catalytic systems, it is very desirable to readily evaluate a
sensitivity analysis for a given model. The result of such a sensitivity
analysis quantitatively expresses the dependency of the turnover frequency,
being the main output variable, on the rate constants entering the model. In
the past, the application of sensitivity analysis, such as degree of rate
control, has been hampered by its exuberant computational effort required to
accurately sample numerical derivatives of a property that is obtained from a
stochastic simulation method. In this study, we present an efficient and
robust three-stage approach that is capable of reliably evaluating the
sensitivity measures for stiff microkinetic models as we demonstrate using the
CO oxidation on RuO2(110) as a prototypical reaction. In the first step, we
utilize the Fisher information matrix for filtering out elementary processes
which only yield negligible sensitivity. Then we employ an estimator based on
the linear response theory for calculating the sensitivity measure for non-
critical conditions which covers the majority of cases. Finally, we adapt a
method for sampling coupled finite differences for evaluating the sensitivity
measure for lattice based models. This allows for an efficient evaluation even
in critical regions near a second order phase transition that are hitherto
difficult to control. The combined approach leads to significant computational
savings over straightforward numerical derivatives and should aid in
accelerating the nano-scale design of heterogeneous catalysts