Application of Evolutionary Strategies in the Experimental Optimization of Catalytic Materials

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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.
Publisher
SPRINGER/PLENUM PUBLISHERS
Issue Date
2010-02
Language
English
Article Type
Article; Proceedings Paper
Keywords

HIGH-THROUGHPUT EXPERIMENTATION; GENETIC ALGORITHM; OXIDATIVE DEHYDROGENATION; NEURAL-NETWORK; COMBINATORIAL; DISCOVERY; SEARCH; WATER

Citation

TOPICS IN CATALYSIS, v.53, no.1-2, pp.2 - 12

ISSN
1022-5528
URI
http://hdl.handle.net/10203/250914
Appears in Collection
CBE-Journal Papers(저널논문)
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