  Many materials processes and properties depend on the anisotropy of the
energy of grain boundaries, i.e.~on the fact that this energy is a function of
the five geometric degrees of freedom (DOF) of the interface. To access this
parameter space in an efficient way and to discover energy cusps in unexplored
regions, a method was recently established, which combines atomistic
simulations with statistical methods 10.1002/adts.202100615. This sequential
sampling technique is now extended in the spirit of an active learning
algorithm by adding a criterion to decide when the sampling has advanced enough
to stop. In this instance, two parameters to analyse the sampling results on
the fly are introduced: the number of cusps, which correspond to the most
interesting and important regions of the energy landscape, and the maximum
change of energy between two sequential iterations. Monitoring these two
quantities provides valuable insight into how the subspaces are energetically
structured. The combination of both parameters provides the necessary
information to evaluate the sampling of the 2D subspaces of grain boundary
plane inclinations of even non-periodic, low angle grain boundaries. With a
reasonable number of data points in the initial design, only a few
appropriately chosen sequential iterations already improve the accuracy of the
sampling substantially and unknown cusps can be found within a few additional
sequential steps.
