Application of Evolutionary Strategies in the Experimental Optimization of Catalytic Materials

Abstract

The issues of heterogeneous catalyst optimization are presented in the framework of high throughput iterative screening. To be efficient, the optimization procedures should consider the limitations of the facilities in terms of screening capacities, experimentation costs, and experimental noise. The issues of algorithm reliability are also addressed. Based on the simulation results, this work highlights the most important features of the evolutionary strategies (ES) that lead to successful optimizations. We show that the monitoring of the population diversity during the optimization is a key parameter. Finally, we provide some best practice recommendations for experimentalists who are not experts in metaheuristic methods and who are willing to apply ES for material library designs.