Machine learned reactive force fields based on polynomial expansions have been shown to
be highly effective for describing simulations involving reactive materials. Nevertheless, the
highly flexible nature of these models can give rise to a large number of candidate
parameters for complicated systems. In these cases, reliable parameterization requires a
well-formed training set, which can be difficult to achieve through standard iterative fitting
methods. Here, we present an active learning approach based on cluster analysis and …