Journal of Computer Chemistry, Japan
Online ISSN : 1347-3824
Print ISSN : 1347-1767
ISSN-L : 1347-1767
Volume 7, Issue 2
Displaying 1-3 of 3 articles from this issue
General Papers
  • Michihisa KOYAMA, Kei OGIYA, Tatsuya HATTORI, Hiroshi FUKUNAGA, Ai SUZ ...
    2008 Volume 7 Issue 2 Pages 55-62
    Published: June 15, 2008
    Released on J-STAGE: June 25, 2008
    Advance online publication: March 15, 2008
    JOURNAL FREE ACCESS
    Irregular porous materials with pore sizes of several tens nm to μm are widely used in industrial applications such as automobile catalysts, gas separation filters, fuel cell electrodes, and lithium ion battery electrodes. Current research and development approaches for irregular porous materials can be classified into the three types shown in Figure 1. Compared to material and interface design approaches, the structure design approach is challenging because no effective method to model realistic porous structures is available presently. To counter this issue, the authors have developed a novel porous structure simulator POCO2, the basic algorithm of which is shown in Figure 2. The POCO2 program is based on an original overlap-allowed particle packing method, where overlaps of particles are allowed up to a certain overlap ratio (Figure 3), and can construct various irregular porous structures as shown in Figure 4. We have also developed tools to quantitatively evaluate microstructures of porous materials, such as cross-sectional area (Figure 5), surface area (Figure 6), pore volume (Figure 7), and triple phase boundary length (Figure 8). In order to investigate the influence of microstructures on the characteristics of irregular porous materials, we have developed a simulator of the overpotential of a solid oxide fuel cell (SOFC) anode (Figure 9). We constructed a model of a Ni-YSZ anode (Figure 10(a)) and confirmed that the overpotential calculated by our simulator agreed well with the experimentally reported value (Figure 10(b)). Finally, we have proposed a scheme for the rational optimization of the microstructure of irregular porous materials (Figure 11(a)) and showed the preliminary results obtained so far (Figure 11(b) and (c)). Based on our preliminary results, we confirmed the potential feasibility of the proposed scheme.
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  • Satoshi FUJISHIMA, Yoshimasa TAKAHASHI, Takashi OKADA
    2008 Volume 7 Issue 2 Pages 63-70
    Published: June 15, 2008
    Released on J-STAGE: June 25, 2008
    Advance online publication: April 24, 2008
    JOURNAL FREE ACCESS
    Studies on the structure–activity relationship of drugs essentially require a relational learning scheme in order to extract meaningful chemical subgraphs; however, most relational learning systems suffer from a vast search space. On the other hand, some propositional logic mining methods use the presence or absence of chemical fragments as features, but rules so obtained give only crude knowledge about part of the pharmacophore structure. This paper proposes a knowledge refinement method in the chemical structure space for the latter approach. A simple hill-climbing approach was shown to be very useful if the seed fragment contains the essential characteristic of the pharmacophore. An application to the analysis of dopamine D1 agonists is discussed as an illustrative example.
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Technical Paper
  • Norio YOSHIMURA, Masao TAKAYANAGI
    2008 Volume 7 Issue 2 Pages 71-82
    Published: June 15, 2008
    Released on J-STAGE: June 25, 2008
    Advance online publication: May 30, 2008
    JOURNAL FREE ACCESS
    Although chemometrics has become widely used recently for analyzing experimental chemical data, there exist in Japan only a few instructions for the proper usage of chemometrics except for some introductory books. We have found that Microsoft Excel (Excel) is convenient, inexpensive, and popular software for chemometrics calculations especially for educational purposes. In this paper, the first of a series on chemometrics calculations with Excel, instructions for matrix calculations and thier applications to multivariate analysis with Excel are described in detail. Array formulas and array calculations are explained in relation to basic handlings of matrix (e.g. calculations of determination and inverse matrices). We also demonstrate applications of array formulas to calculations of polynomial curves, and to curve fittings.
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