- Tytuł:
- Prediction of the Density of Energetic Co-crystals: a Way to Design High Performance Energetic Materials
- Autorzy:
-
Zohari, Narges
Mohammadkhani, Faezeh Ghiasvand - Powiązania:
- https://bibliotekanauki.pl/articles/357956.pdf
- Data publikacji:
- 2020
- Wydawca:
- Sieć Badawcza Łukasiewicz - Instytut Przemysłu Organicznego
- Tematy:
-
energetic co-crystals
density
MLR method
artificial neural network
molecular design - Opis:
- 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.
- Źródło:
-
Central European Journal of Energetic Materials; 2020, 17, 1; 31-48
1733-7178 - Pojawia się w:
- Central European Journal of Energetic Materials
- Dostawca treści:
- Biblioteka Nauki
Artykuł