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Prediction of the Density of Energetic Co-crystals: a Way to Design High Performance Energetic Materials

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
For designing a new energetic material with good performance, a knowledge of its density is required. In this study, the relationship between the densities of energetic co-crystals and their molecular structures was examined through a quantitative structure-property relationship (QSPR) method. The methodology of this research provides a new model which can relate the density of an energetic co-crystal to several molecular structural descriptors, which are calculated by Dragon software. It is indicated that the density of a co-crystal is a function of sp, OB, DU, nAT, as well as several non-additive structural parameters. The new recommended correlation was derived on the basis of the experimental densities of 50 co-crystals with various structures as a training set. The R2 or determination coefficient of the derived correlation was 0.937. This correlation provided a suitable estimate for a further 12 energetic co-crystals as a test set. Meanwhile, the predictive ability of the correlation was investigated through a cross validation method. Moreover, the new model has more reliability and performance for predicting the densities of energetic co-crystals compared to a previous one which was based on an artificial neural network approach. As a matter of fact, designing novel energetic co-crystals is possible by utilising the proposed method.
Rocznik
Strony
31--48
Opis fizyczny
Bibliogr. 53 poz., rys., tab.
Twórcy
  • Faculty of Chemistry and Chemical Engineering, Malek-ashtar University of Technology, Tehran, Iran
  • Faculty of Chemistry and Chemical Engineering, Malek-ashtar University of Technology, Tehran, Iran
Bibliografia
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Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-ca5cc332-64c3-4ac8-adac-df32cb5acddd
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