(563c) Prediction of Environmental Impact of Energetic Materials in Silico

The objective of this research is to determine how some energetic materials behave in the environment. The fate of any compound in soil, water or the atmosphere can be determined by predicting the partition or distribution coefficient. Two key partition coefficients used by the scientific community to assess a compound's impact in the environment are octanol-water partition coefficients and Henry's law constants (air-water partition coefficient). A wide variety of experimental techniques exist to measure the partition coefficient but atomistic simulation is attractive as a predictive tool for studying the environmental impact of these compounds due to three primary reasons: the hazardous nature of the explosives, long experimental time scales involved in testing of the materials and the costs associated with it. Furthermore, compounds can be evaluated for environmental hazards via simulation before synthesis, potentially leading to significant cost savings.

In this work force fields (molecular models) have been developed for six energetic compounds, 2,4-dinitroanisole (DNAN), N-methyl-p-nitroaniline (MNA), dinitropyrazole (DNP), nitrotriazolone (NTO), methyl-trinitroimidazole (MTNI) and TATB (trinitrotriaminobenzene) categorized by the army as potential insensitive munitions compounds. These force fields are used to predict pharmacokinetic properties such as octanol-water partition coefficients and Henry's law constants along with other thermophysical properties such as vapor-liquid equilibria, vapor pressure, critical parameters and normal boiling points. The validation of the developed force fields is accomplished with the scarce experimental data available. The octanol-water partition coefficients and Henry's law constant predicted for DNAN and MNA are in good agreement with the experiment. For DNP, NTO, MTNI and TATB, materials for which limited experimental data are available, the predicted partition coefficients agree well with values calculated with group contribution methods.