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

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.