Development of quantitative structure-property relationships for predictive modeling and design of energetic materials.
ABSTRACT A quantitative structure-property relationship (QSPR) based on the AM1 semiempirical quantum mechanical method was derived using the program, CODESSA, to describe published drop height impact sensitivities for 227 nitroorganic compounds. An eight-descriptor correlation equation having R(2)=0.8141 was obtained through a robust least median squares regression. The resulting model is the most comprehensive and systematic quantum mechanically derived QSPR for energetic materials of those that have been published. The predictive capability of the model is also presented and discussed.
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ABSTRACT: New quantitative structure property relationships (QSPR) have been developed to predict accurately the impact sensitivity of nitro compounds from their molecular structures. Such predictive approaches represent good alternative to complete experimental testing in development process or for regulatory issues (e.g. within the European REACH regulation). To achieve highly predictive models, two approaches were used to explore the whole diversity of nitro compounds included in a data set of 161 molecules. In a first step, local models, dedicated to the nitramines, nitroaliphatics and nitroaromatics, were proposed. After that, a global model was developed to be applicable for the whole range of the nitro compounds of the data set. In both cases, large series of molecular descriptors were calculated from quantum chemically calculated molecular structures and multilinear regressions were computed to correlate them with experimental impact sensitivities. All proposed models were validated for regulatory use according to the OECD principles, including internal, external validation and the definition of their applicability domain. So, they could then be used for prediction separately or in a consensus approach.2012 AIChE Spring National Meeting; 04/2012
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ABSTRACT: A quantitative structure–property relationship model was built to predict the impact sensitivity of 186 nonheterocyclic nitroenergetic compounds. The genetic algorithm was employed to select an optimal subset of descriptors that significantly contribute to the impact sensitivity. A nonlinear artificial neural network was employed to fit a possible relationship between the selected descriptors and impact sensitivity. The results are satisfactory for prediction capability, robustness, and generalization. The proposed method can be used to predict the impact sensitivity of nonheterocyclic nitro compounds based on knowledge of the molecular structures.Journal of Energetic Materials 04/2012; 30(2):135-155. · 1.34 Impact Factor
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ABSTRACT: For registration of a chemical, European Union REACH legislation requires information on the relevant physico-chemical properties of the chemical. Predicted property values can be used when the predictions can be shown to be valid and adequate. The relevant physico-chemical properties that are amenable to prediction are: melting/freezing point, boiling point, relative density, vapour pressure, surface tension, water solubility, n-octanol-water partition coefficient, flash point, flammability, explosive properties, self-ignition temperature, adsorption/desorption, dissociation constant, viscosity, and air-water partition coefficient (Henry's law constant). Published quantitative structure-property relationship (QSPR) methods for all of these properties are discussed, together with relevant property prediction software, as an aid for those wishing to use predicted property values in submissions to the European Chemicals Agency (ECHA).SAR and QSAR in environmental research 03/2013; · 1.68 Impact Factor