Local and semilocal density functional computations for crystals of 1-alkyl-3-methyl-imidazolium salts

The accuracy and reliability of popular density functional approximations for the compounds giving origin to room temperature ionic liquids have been assessed by computing the T=0 K crystal structure of several 1-alkyl-3-methyl-imidazolium salts. Two prototypical exchange-correlation approximations have been considered, i.e., the local density approximation (LDA) and one gradient corrected scheme [PBE-GGA, Phys. Rev. Lett. 77, 3865 (1996)]. Comparison with low-temperature x-ray diffraction data shows that the equilibrium volume predicted by either approximations is affected by large errors, nearly equal in magnitude (~10%), and of opposite sign. In both cases the error can be traced to a poor description of the intermolecular interactions, while the intramolecular structure is fairly well reproduced by LDA and PBE-GGA. The PBE-GGA optimization of atomic positions within the experimental unit cell provides results in good agreement with the x-ray structure. The correct system volume can also be restored by supplementing PBE-GGA with empirical dispersion terms reproducing the r-6 attractive tail of the van der Waals interactions.

Learning soft computing control strategies in a modular neural network architecture

Modelling and control of nonlinear dynamical systems is a challenging problem since the dynamics of such systems change over their parameter space. Conventional methodologies for designing nonlinear control laws, such as gain scheduling, are effective because the designer partitions the overall complex control into a number of simpler sub-tasks. This paper describes a new genetic algorithm based method for the design of a modular neural network (MNN) control architecture that learns such partitions of an overall complex control task. Here a chromosome represents both the structure and parameters of an individual neural network in the MNN controller and a hierarchical fuzzy approach is used to select the chromosomes required to accomplish a given control task. This new strategy is applied to the end-point tracking of a single-link flexible manipulator modelled from experimental data. Results show that the MNN controller is simple to design and produces superior performance compared to a single neural network (SNN) controller which is theoretically capable of achieving the desired trajectory. (C) 2003 Elsevier Ltd. All rights reserved.

Physics of electron beam ion traps and sources

This paper presents the basic physics underlying the operation of electron beam ion traps and sources, with the machine physics underlying their operation being described in some detail. Predictions arising from this description are compared with some diagnostic measurements.