  Surrogate models for hotspot ignition and growth rates were presented in Part
I, where the hotspots were formed by the collapse of single cylindrical voids.
Such isolated cylindrical voids are idealizations of the void morphology in
real meso-structures. This paper therefore investigates the effect of
non-cylindrical void shapes and void-void interactions on hotspot ignition and
growth. Surrogate models capturing these effects are constructed using a
Bayesian Kriging approach. The training data for machine learning the
surrogates are derived from reactive void collapse simulations spanning the
parameter space of void aspect ratio (AR), void orientation ($\theta$), and
void fraction ($\phi$). The resulting surrogate models portray strong
dependence of the ignition and growth rates on void aspect ratio and
orientation, particularly when they are oriented at acute angles with respect
to the imposed shock. The surrogate models for void interaction effects show
significant changes in hotspot ignition and growth rates as the void fraction
increases. The paper elucidates the physics of hotspot evolution in void fields
due to the creation and interaction of multiple hotspots. The results from this
work will be useful not only for constructing meso-informed macro-scale models
of HMX, but also for understanding the physics of void-void interactions and
sensitivity due to void shape and orientation.
