Journal of Computer Chemistry, Japan -International Edition
Online ISSN : 2189-048X
ISSN-L : 2189-048X
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  • Toshiaki MATSUBARA, Keisuke SHIRASAKA
    2021 Volume 7 2020-0003
    Published: 2021
    Released: June 11, 2021
    Supplementary material

    Recently, the synthesis of phosphinophosphinidene, which is a phosphorus analog of carbene, has been reported. Subsequent experimental reports have shown that phosphinophosphinidene acts as an electron acceptor. Because the terminal phosphorus atom inherently acts as an electron donor, chemical reactions may lead to the σ bond cleavage at the phosphorus atom through charge-transfer interaction. In this study, we explore the possibility of the σ bond cleavage in H–H, C–H, O–H, N–H, and B–H bonds by means of the density functional method using the model molecules, H2, CH4, H2O, NH3 and BH3. For H2 and CH4, the H–H and the C–H bonds were found to be broken at the single site of the terminal phosphorus atom by the charge-transfer interactions. The potential energy barrier of about 22–24 kcal/mol is similar to that for carbene, suggesting the possibility of σ bond cleavage in phosphinophosphinidene. In contrast, for H2O and NH3, the O–H and N–H bonds are broken at the two sites of both phosphorus atoms by the abstraction of hydrogen as a proton. In the case of BH3, cleavage of the B–H bond occurs easily at both the single and dual sites of the phosphorus atoms.

  • Yuko IKEDA, Michihiro OKUYAMA, Yukihito NAKAZAWA, Tomohiro OSHIYAMA, K ...
    2021 Volume 7 2020-0007
    Published: 2021
    Released: June 11, 2021

    Advanced processes are useful when developing polymer composites because there are an enormous number of possible combinations of fillers and additives to realize polymers with desired properties. Materials informatics is a data-driven approach to find novel materials or a suitable combination of materials from material data sheets. Here, we used materials informatics to construct a predictive model for the elastic modulus of polypropylene composites. To apply materials informatics to existing experimental data, we described explanatory variables by a combination of 0 and 1 representing polypropylene, or by the content ratio of filler and additive, without using materials property data. We constructed a predictive model for the elastic modulus of polypropylene composites using a partial least square regression model with dummy variables. To validate the predictive model, comparisons were made between measured and predicted elastic moduli for eight new polypropylene composites. The residual was less than 300 MPa for the range 1,000–3,000 MPa. We improved the accuracy of the prediction for composites with high filler content ratio by applying a nonlinear support vector regression model. The predictive model is therefore useful for identifying suitable combinations of polypropylene, filler and additive to achieve a desired elastic modulus.