Applications of Evolutionary Computation in Chemistry by Roy L. Johnston, H.M. Cartwright, V.J. Gillet, S. Habershon, PDF

By Roy L. Johnston, H.M. Cartwright, V.J. Gillet, S. Habershon, K.D.M. Harris, B. Hartke, R.L. Johnston, R. Unger, S. Woodley

ISBN-10: 3540402586

ISBN-13: 9783540402589

H. M. Cartwright: An creation to Evolutionary Computation andEvolutionary Algorithms; B. Hartke: program of Evolutionary Algorithms to international Cluster Geometry Optimization; K.D.M. Harris, R.L. Johnston, S. Habershon: software of Evolutionary Computation in constitution resolution from Diffraction facts; S. M.

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39 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 40 42 43 44 44 . . . . . . . . . . . 45 . . . . . . . . . . . . . . . . 50 Abbreviations AM1 CSA DFT DFTB EA Semiempirical Austin method 1 Conformational space annealing Density functional theory Density-functional-based tight binding Evolutionary algorithm © Springer-Verlag Berlin Heidelberg 2004 34 B. Hartke GGA HF LMP2 LJ Generalized gradient approximation (within density functional theory) Hartree–Fock Local second-order Møller–Plesset perturbation theory Lennard-Jones (interparticle potential; used like a chemical symbol for a single atom in this article) MC Monte Carlo MD Molecular dynamics NP Nondeterministic polynomial (problem complexity level) PES Potential-energy surface SAS imulated annealing SPC/E Simple point charge, extended(empirical water potential) TB Tight binding TIP3P Transferable intermolecular potential with three points (one of Jorgensen’s empirical intermolecular water potentials) TIP4P Transferable intermolecular potential with four points (another of Jorgensen’s empirical intermolecular water potentials) UHF Unrestricted Hartree–Fock n Number of atoms or molecules in a cluster m Population size (number of individuals per generation) 1 Introduction The modern research area of nanotechnology aims at controlled fabrication and technical use of aggregates of atoms and molecules with typical length scales of nanometers, by making traditional devices smaller and smaller.

Isolated Molecular Clusters . . . . 4 Comparison to Other Methods 5 Current and Future Method Development . . . . . . . . 48 6 References . . . . . . . . . . . . . . 39 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 40 42 43 44 44 . . . . . . . . . . . 45 . . . . . . . . . . . . . . . . 50 Abbreviations AM1 CSA DFT DFTB EA Semiempirical Austin method 1 Conformational space annealing Density functional theory Density-functional-based tight binding Evolutionary algorithm © Springer-Verlag Berlin Heidelberg 2004 34 B.

Therefore, additional evolutionary operators may be introduced that directly change this atom-type distribution without affecting the atom positions in space. For the resulting structures, additional questions can be asked: How and how strongly do optimal structures change with cluster composition? Do the different atom types prefer to mix or to segregate within the clusters? These issues were addressed in mixed-cluster studies for n£56 with Gupta potentials, for the systems Pd/Pt [92] and Cu/Au [93].

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Applications of Evolutionary Computation in Chemistry by Roy L. Johnston, H.M. Cartwright, V.J. Gillet, S. Habershon, K.D.M. Harris, B. Hartke, R.L. Johnston, R. Unger, S. Woodley


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