Modeling Sensitivities of Energetic Materials using the Python Language and Libraries

Assessing 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...

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Published in:Propellants, explosives, pyrotechnics Vol. 45; no. 6; pp. 966 - 973
Main Author: Mathieu, Didier
Format: Journal Article
Language:English
Published: Weinheim Wiley Subscription Services, Inc 01.06.2020
Subjects:
Computer program, QSPR
Electric sparks
Energetic materials
Explosive
Libraries
Molecular modeling
Molecular structure
Programming languages
Sensitivity
ISSN:0721-3115, 1521-4087
Online Access:Get full text
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Abstract Assessing 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.
AbstractList Assessing 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.
Author Mathieu, Didier
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SubjectTerms Computer program, QSPR
Electric sparks
Energetic materials
Explosive
Libraries
Molecular modeling
Molecular structure
Programming languages
Sensitivity
Title Modeling Sensitivities of Energetic Materials using the Python Language and Libraries
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