Chemical Descriptors for a Large-Scale Study on Drop-Weight Impact Sensitivity of High Explosives

Abstract

The drop-weight impact test is an experiment that has been used for nearly 80 years to evaluate handling sensitivity of high explosives. Although the results of this test are known to have large statistical uncertainties, it is one of the most common tests due to its accessibility and modest material requirements. In this paper, we compile a large data set of drop-weight impact sensitivity test results (mainly performed at Los Alamos National Laboratory), along with a compendium of molecular and chemical descriptors for the explosives under test. These data consist of over 500 unique explosives, over 1000 repeat tests, and over 100 descriptors, for a total of about 1500 observations. We use random forest methods to estimate a model of explosive handling sensitivity as a function of chemical and molecular properties of the explosives under test. Our model predicts well across a wide range of explosive types, spanning a broad range of explosive performance and sensitivity. We find that properties related to explosive performance, such as heat of explosion, oxygen balance, and functional group, are highly predictive of explosive handling sensitivity. Yet, models that omit many of these properties still perform well. Our results suggest that there is not one or even several factors that explain explosive handling sensitivity, but that there are many complex, interrelated effects at play.

Figure 1

Electronic eigenspectra of PETN and 2,4,6-TNT from the ground state DFTB electronic structure. The energies of the HOMO, LUMO, and center of mass, , are highlighted. 2,4,6-TNT has a slightly wider eigenspectrum than PETN, which is measured by the normalized second moment, . Both eigenspectra exhibit positive skewness, , which is larger for 2,4,6-TNT, and moderately unimodal distributions, s ≈ 1.

Figure 2

Drop energy trends with the strongest related categorical and continuous descriptors, functional groups, and oxygen balances, respectively.

Figure 3

Spearman correlations of descriptors most strongly correlated with logE₅₀, ordered by the absolute value of this correlation.

Figure 4

(Left) Average RMSE_η across random cross validation partitions, as a function of η. Dashed lines denote models trained on data consisting only of mean drop energies of unique molecules (“Means data”), and solid lines denote models trained on data including all repeat observations (“All data”). (Right) Average predicted drop energy (logE₅₀), across 10 random cross-validations, from VSURF method trained all data, compared to observed mean drop energy for all unique molecules. Molecules with five or more repeated observations are highlighted in color.

Figure 5

Mean cross-validation performance of the selected model when adding each descriptor in turn (“forward”) and removing each descriptor in turn (“backwards”).

Figure 6

Estimated average effects of oxygen balance (left) and Moment1 (right), separated by functional group.

Figure 7

Residuals (observed drop energy less predicted drop energy) for each functional group.