Development of a Genetic Algorithm for Molecular Scale Catalyst Design

A genetic algorithm has been developed to determine the optimal design of a two-component catalyst for the diffusion-limitedA+B → AB↑ reaction in which each species is adsorbed specifically on one of two types of sites. Optimisation of the distribution of catalytic sites on the surface is achieved by means of an evolutionary algorithm which repeatedly selects the more active surfaces from a population of possible solutions leading to a gradual improvement in the activity of the catalyst surface. A Monte Carlo simulation is used to determine the activity of each of the catalyst surfaces. It is found that for a reacting mixture composed of equal amounts of each component the optimal active site distribution is that of a checkerboard, this solution being approximately 25% more active than a random site distribution. Study of a range of reactant compositions has shown the optimal distribution of catalytically active sites to be dependent on the composition of the ratio ofAtoBin the reacting mixture. The potential for application of the optimisation method introduced here to other catalysts systems is discussed.

[1]  G. Somorjai,et al.  Heterogeneous catalysis on the molecular scale , 1982 .

[2]  J. Sinfelt,et al.  Bimetallic Catalysts: Discoveries, Concepts, and Applications , 1983 .

[3]  G. Somorjai,et al.  Influence of ensemble size on CO chemisorption and catalytic n-hexane conversion by Au-Pt(111) bimetallic single-crystal surfaces , 1983 .

[4]  Redner,et al.  Fluctuation-dominated kinetics in diffusion-controlled reactions. , 1985, Physical review. A, General physics.

[5]  Fractal clustering of reactants on a catalyst surface. , 1986, Physical review. B, Condensed matter.

[6]  I. Alstrup,et al.  Statistical models for ensemble control by alloying and poisoning of catalysts: II. Comparisons with Monte Carlo simulations and with experimental results , 1987 .

[7]  D. T. Lynch,et al.  Monte Carlo simulation of supported metal catalyst sintering and redispersion , 1988 .

[8]  R. Ziff,et al.  Noise-induced bistability in a Monte Carlo surface-reaction model. , 1989, Physical review letters.

[9]  Michael D. Vose,et al.  Generalizing the Notion of Schema in Genetic Algorithms , 1991, Artif. Intell..

[10]  A. Davidlogan.  Steady-state co oxidation kinetics over the Pd(100) single crystal surface and the c(2 × 2)-Sn/Pd(100) bimetallic surface alloy , 1992 .

[11]  Self-organization in reactions at surfaces , 1993 .

[12]  R. Herz,et al.  Two-Component Catalysts for Low-Temperature CO Oxidation: A Monte Carlo Study , 1993 .

[13]  Yong L. Xiao,et al.  Genetic algorithm: a new approach to the prediction of the structure of molecular clusters , 1993 .

[14]  Trigger Mechanism of Self-Oscillations and Effect of Molecule Self-Organization in the Course of Monte Carlo Modeling of a Bimolecular Catalytic Reaction , 1993 .

[15]  K. H. Lee,et al.  A novel microfabricated Pd/SiO2 model catalyst for the hydrogenation of 1,3-butadiene , 1994 .

[16]  G. Zgrablich,et al.  The role of surface reconstruction in catalytic activity , 1994 .

[17]  D. Goodman,et al.  Kinetics of CO oxidation on Cu/Rh(100) model bimetallic catalysts , 1994 .

[18]  Arun S. Mujumdar,et al.  Introduction to Surface Chemistry and Catalysis , 1994 .

[19]  L. D. Hall,et al.  Design by genetic algorithm of a z gradient set for magnetic resonance imaging of the human brain , 1995 .

[20]  Heterogeneous catalysis on a disordered surface. , 1995, Physical review letters.

[21]  J. Rodríguez,et al.  Physical and chemical properties of bimetallic surfaces , 1996 .