Modeling Sensitivities of Energetic Materials using the Python Language and Libraries

Abstract

AbstractAssessing the value of new compounds as components of energetic materials requires the determination of a significant amount of data, including sensitivities to various stimuli. Unfortunately, the dependence of these properties on molecular structure is still poorly understood. In view of estimating their values for putative high energy molecules, standard quantitative structure‐property relationship (QSPR) methodologies are widely used. In doing so, a special focus is put on standard descriptors and formalisms. To foster further progress through consideration of alternative approaches, this article emphasizes how the Python language and associated libraries make it straightforward to implement arbitrary models, including schemes à la Keshavarz based on the occurrences of highly specific molecular fragments as well as the non‐linear expressions naturally arising from physics‐based approaches to sensitivities. Two previously published models are implemented for illustrative purposes. The first one is a simple fragment‐based equation for electric spark sensitivity of nitroarenes. The second one is a model for impact sensitivity of general molecular energetic materials. In each case a Python implementation is provided as supporting information and may be used as is or serve as a template to implement alternative schemes.