Sensitivities of High Energy Compounds

Zeman, Svatopluk

doi:10.1007/430_2006_052

Cited by 101 publications

(91 citation statements)

References 122 publications

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“…[75,76] In polynitro compounds, for example, the oxygen atoms of the nitro groups, by their dipole-dipole interactions, contact the oxygen and nitrogen atoms of nitro groups in neighboring nitramine molecules in the crystal, [77][78][79] which is the decisive factor governing the crystal structure of nitramines. Nonbinding interatomic distances of oxygen atoms in the nitro compound molecules are shorter than those for the intermolecular contact radii for oxygen in carbonyl or nitro groups (i.e., 1.35-1.63 Å [80] ).…”

Section: Correlation Verification For the Predicted Resultsmentioning

confidence: 99%

“…[31] Elimination of this incorrect selection is possible based on verifying the correlation of the given sensitivity data with the corresponding molecular structure. [75,[84][85][86] In summary, different methods for impact sensitivity evaluation are suitable for different groups of energetic compounds. As stated above, for instance, the Bond Dissociation Energy method is only applicable for compounds with simple structures based on C, H, N, O compositions, while the Molecular Modeling method is only applicable for inorganic azides.…”

Section: Correlation Verification For the Predicted Resultsmentioning

confidence: 99%

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Theoretical evaluation of sensitivity and thermal stability for high explosives based on quantum chemistry methods: A brief review

Yan

Zeman

2012

Int J of Quantum Chemistry

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114

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Over the past 20 years, a number of scientists have conducted numerous fundamental investigations based on quantum chemistry theory into various mechanistic processes that seems to contribute to the sensitivity of energetic materials. A large number of theoretical methods that have been used to predict their mechanical and spark sensitivity are summarized in this article, in which the advantages and disadvantages of these methods, together with their scope of use are clarified. In addition, the theoretical models for thermal stability of explosives are briefly introduced as a supplement. It has been concluded that the current ability to predict sensitivity is merely based on a series of empirical rules, such as simple oxygen balance, molecular properties, and the ratios of C and H to oxygen for different classes of explosive compounds. These are valid only for organic classes of explosives, though some special models have been proposed for inorganic explosives, such as azides. An exact standard for sensitivity should be established experimentally by some new techniques for both energetic compounds and their mixtures. © 2013 Wiley Periodicals, Inc.

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Section: Correlation Verification For the Predicted Resultsmentioning

confidence: 99%

Section: Correlation Verification For the Predicted Resultsmentioning

confidence: 99%

Theoretical evaluation of sensitivity and thermal stability for high explosives based on quantum chemistry methods: A brief review

Yan

Zeman

2012

Int J of Quantum Chemistry

Self Cite

114

View full text Add to dashboard Cite

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“…This larger Q has been labeled Q max [4,42]. It is Q max that is related to sensitivity [38,[40][41][42][43]49].…”

Section: Properties Governing Detonation Velocity and Detonation Presmentioning

confidence: 99%

“…While increasing Q does improve detonation performance, as measured by D and P, it also tends to be accompanied by the very undesirable feature of increasing sensitivity − the vulnerability of the explosive to accidental detonation due to unintended stimuli, such as impact, shock, etc. There is an overall trend for larger heat release to be associated with greater sensitivity [38,[40][41][42][43]49]. Thus the price for better detonation performance has often been a more sensitive explosive [31,42,43,45,46].…”

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confidence: 99%

Nitro Groups vs. N-Oxide Linkages: Effects Upon Some Key Determinants of Detonation Performance

Politzer¹,

Murray²

2017

Cent. Eur. J. Energ. Mater.

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Abstract:Increasing the nitrogen/carbon ratios in the molecular frameworks of C,H,N,O explosives has attracted considerable attention because it tends to result in more positive heats of formation and often greater densities. In conjunction with this, there has been a growing interest in N-oxide linkages,, as another source of oxygen in these compounds, in addition to or even possibly replacing NO2 groups. In this study, for a series of polyazines and polyazoles, we have compared the effects of introducing a single N-oxide linkage or NO2 group upon key properties that affect detonation velocity and detonation pressure. We found that: (1) The heats of formation per gram of compound, which is what is relevant for this purpose, are almost always higher for the N-oxides. (2) The nitro derivatives have greater densities and detonation heat releases. In relation to the latter, it must be kept in mind that increasing detonation heat release tends to be accompanied by increasing sensitivity. (3) The N-oxides produce more moles of gaseous detonation products per gram of compound.

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“…35 Similarly, insights gained from the interpretation of such models may not generalize and may be contaminated by spurious correlations that often occur in small datasets. The part of chemical space where a model yields accurate predictions is known as the applicability domain.…”

Section: Machine Learning Performance Vs Training Data Quantity and DImentioning

confidence: 99%

Applying Machine Learning Techniques to Predict the Properties of Energetic Materials

Elton¹,

Boukouvalas²,

Butrico³

et al. 2018

Preprint

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We present a proof of concept that machine learning techniques can be used to predict the properties of CNOHF energetic molecules from their molecular structures. We focus on a small but diverse dataset consisting of 109 molecular structures spread across ten compound classes. Up until now, candidate molecules for energetic materials have been screened using predictions from expensive quantum simulations and thermochemical codes. We present a comprehensive comparison of machine learning models and several molecular featurization methods -sum over bonds, custom descriptors, Coulomb matrices, Bag of Bonds, and fingerprints. The best featurization was sum over bonds (bond counting), and the best model was kernel ridge regression. Despite having a small data set, we obtain acceptable errors and Pearson correlations for the prediction of detonation pressure, detonation velocity, explosive energy, heat of formation, density, and other properties out of sample. By including another dataset with ≈ 300 additional molecules in our training we show how the error can be pushed lower, although the convergence with number of molecules is slow. Our work paves the way for future applications of machine learning in this domain, including automated lead generation and interpreting machine learning models to obtain novel chemical insights.

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Sensitivities of High Energy Compounds

Cited by 101 publications

References 122 publications

Theoretical evaluation of sensitivity and thermal stability for high explosives based on quantum chemistry methods: A brief review

Theoretical evaluation of sensitivity and thermal stability for high explosives based on quantum chemistry methods: A brief review

Nitro Groups vs. N-Oxide Linkages: Effects Upon Some Key Determinants of Detonation Performance

Applying Machine Learning Techniques to Predict the Properties of Energetic Materials

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