In the search for new compositions of materials of maximal catalytic performance, various methods have been developed when using high-throughput experimentation for preparing and testing of catalysts. The methods are discussed along with the analysis of data obtained in experimentation.

For finding potential catalytic materials, a subset of representatives conveying the required information about all possibilities was addressed in the past by statistical design of experiments (DoE). DoE methods assume that interesting catalytic materials are uniformly distributed in the compositional space. If interesting materials form instead of only one cluster several small clusters in the compositional space, methods of function optimization should be used. Most successful in the search of optimal catalytic materials have been evolutionary, i.e. a biologically inspired, stochastic optimisation methods. Their most well-known representatives are genetic algorithms (GA), in which the incorporation of random influences attempts to mimic the evolution of a genotype. Basically, that method comprises:
- random crossover; - random mutation,

- selecting the locations for cross-over and mutation (parent locations) according to a probability distribution.

Subsequent data analysis may be done by artificial neural networks (ANN), which are distributed computing systems attempting to implement a part of the functionality of biological neural networks. From the point of view of data analysis, their most valuable feature is the ability to approximate arbitrarily complex dependences, such as the dependence of catalytic performance on the composition of the material and its various physical and physicochemical properties. In this way, new insights into the science of heterogeneous catalysis may be obtained.

Applications of GA and ANN are illustrated for the development of catalytic materials for several reactions.

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1. Application of genetic algorithms in the development of catalytic inorganic materials