Application of data mining and evolutionary optimization in catalyst discovery and high-throughput experimentation techniques, strategies, and software

Data-mining and evolutionary optimization techniques are powerful tools to improve the efficiency of high-throughput experimentation (HTE) to discover new materials, drugs, or catalysts. The parameter space of screening experiments is usually high-dimensional and the parameters are possibly discrete. The response surface of the screened systems can be very rugged, characterized by smooth planes as well as steep and narrow ascents of abundant sub-optima. These conditions make exclusive use of classical statistical design and data analysis inappropriate. Evolutionary strategies, neural networks, and data mining may be an efficient alternative. Using two examples, we show the practical benefit of design strategies which combine different techniques. The selection of the methods depends on the nature of the respective HTE problem. An optimal design strategy makes HTE more efficient, and reduces research costs and time to market. Furthermore, the early application of design strategy enables reliable statements about the feasibility of the research project.