  Twinning is an important deformation mode in plastically deformed hexagonal
close-packed materials. The extremely high twin growth rates at the nanoscale
make atomistic simulations an attractive method for investigating the role of
individual twin/matrix interfaces such as twin boundaries and basal-prismatic
interfaces in twin growth kinetics. Unfortunately, there is no single framework
that allows researchers to differentiate such interfaces automatically, neither
in experimental in-situ transmission electron microscopy analysis images nor in
atomistic simulations. Moreover, the presence of alloying elements introduces
substantial noise to local atomic environments, making it nearly impossible to
identify which atoms belong to which interface. Here, with the help of advanced
machine learning methods, we provide a proof-of-concept way of using the local
stress field distribution as an indicator for the presence of interfaces and
for determining their types. We apply such an analysis to the growth of twin
embryos in Mg-10 at.% Al alloys under constant stress and constant strain
conditions, corresponding to two extremes of high and low strain rates,
respectively. We discover that the kinetics of such growth is driven by
high-energy basal-prismatic interfaces, in line with our experimental
observations for pure Mg.
