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Many materials processes and properties depend on the anisotropy of theenergy of grain boundaries, i.e.~on the fact that this energy is a function ofthe five geometric degrees of freedom (DOF) of the interface. To access thisparameter space in an efficient way and to discover energy cusps in unexploredregions, a method was recently established, which combines atomisticsimulations with statistical methods 10.1002/adts.202100615. This sequentialsampling technique is now extended in the spirit of an active learningalgorithm by adding a criterion to decide when the sampling has advanced enoughto stop. In this instance, two parameters to analyse the sampling results onthe fly are introduced: the number of cusps, which correspond to the mostinteresting and important regions of the energy landscape, and the maximumchange of energy between two sequential iterations. Monitoring these twoquantities provides valuable insight into how the subspaces are energeticallystructured. The combination of both parameters provides the necessaryinformation to evaluate the sampling of the 2D subspaces of grain boundaryplane inclinations of even non-periodic, low angle grain boundaries. With areasonable number of data points in the initial design, only a fewappropriately chosen sequential iterations already improve the accuracy of thesampling substantially and unknown cusps can be found within a few additionalsequential steps.

Towards active learning: A stopping criterion for the sequential sampling of grain boundary degrees of freedom