Efficient discovery of nonlinear dependencies in a combinatorial catalyst data set.

Exploration of a complex catalyst system using Genetic Algorithm methods and combinatorial experimentation efficiently removes noncontributing elements and generates data that can be used to model the remaining system. In particular the combined methods effectively navigate and optimize systems with highly nonlinear dependencies (3-way and higher interactions).