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Volume 211, 2018
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Zeolite structure determination using genetic algorithms and geometry optimisation

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Abstract

The recently presented software zeoGAsolver is discussed, which is based on genetic algorithms, with domain-dependent crossover and selection operators that maintain the size of the population in successive iterations while improving the average fitness. Using the density, cell parameters, and symmetry (or candidate symmetries) of a zeolite sample whose resolution can not be achieved by analysis of the XRD (X-ray diffraction) data, the software attempts to locate the coordinates of the T-atoms of the zeolite unit cell employing a function of ‘fitness’ (F), which is defined through the different contributions to the ‘penalties’ (P) as F = 1/(1 + P). While testing the software to find known zeolites such as LTA (zeolite A), AEI (SSZ-39), ITW (ITQ-12) and others, the algorithm has found not only most of the target zeolites but also seven new hypothetical zeolites whose feasibility is confirmed by energetic and structural criteria.

Graphical abstract: Zeolite structure determination using genetic algorithms and geometry optimisation

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Publication details

The article was received on 17 Feb 2018, accepted on 13 Apr 2018 and first published on 13 Apr 2018


Article type: Paper
DOI: 10.1039/C8FD00035B
Citation: Faraday Discuss., 2018,211, 103-115
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    Zeolite structure determination using genetic algorithms and geometry optimisation

    X. Liu, S. Valero, E. Argente and G. Sastre, Faraday Discuss., 2018, 211, 103
    DOI: 10.1039/C8FD00035B

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