The recently developed iterated stockholder atoms (ISA) approach of Lillestolen and Wheatley (Chem. Commun. 2008, 5909) offers a powerful method for defining atoms in a molecule. However, the real-space algorithm is known to converge very slowly, if at all. Here, we present a robust, basis-space algorithm of the ISA method and demonstrate its applicability on a variety of systems. We show that this algorithm exhibits rapid convergence (taking around 10-80 iterations) with the number of iterations needed being unrelated to the system size or basis set used. Further, we show that the multipole moments calculated using this basis-space ISA method are as good as, or better than, those obtained from Stone's distributed multipole analysis (J. Chem. Theory Comput. 2005, 1, 1128), exhibiting better convergence properties and resulting in better behaved penetration energies. This can have significant consequences in the development of intermolecular interaction models.
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http://dx.doi.org/10.1021/ct5008444 | DOI Listing |
Neuroimage
November 2018
Department of Biostatistics, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA.
Event-related potentials (ERPs) summarize electrophysiological brain response to specific stimuli. They can be considered as correlated functions of time with both spatial correlation across electrodes and nested correlations within subjects. Commonly used analytical methods for ERPs often focus on pre-determined extracted components and/or ignore the correlation among electrodes or subjects, which can miss important insights, and tend to be sensitive to outlying subjects, time points or electrodes.
View Article and Find Full Text PDFIEEE Trans Neural Netw Learn Syst
June 2017
In this paper, we propose a new unsupervised spectral feature selection model by embedding a graph regularizer into the framework of joint sparse regression for preserving the local structures of data. To do this, we first extract the bases of training data by previous dictionary learning methods and, then, map original data into the basis space to generate their new representations, by proposing a novel joint graph sparse coding (JGSC) model. In JGSC, we first formulate its objective function by simultaneously taking subspace learning and joint sparse regression into account, then, design a new optimization solution to solve the resulting objective function, and further prove the convergence of the proposed solution.
View Article and Find Full Text PDFJ Chem Theory Comput
December 2014
School of Physics and Astronomy, Queen Mary University of London, London E1 4NS, United Kingdom.
The recently developed iterated stockholder atoms (ISA) approach of Lillestolen and Wheatley (Chem. Commun. 2008, 5909) offers a powerful method for defining atoms in a molecule.
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