  This letter presents a novel approach for identifying uncorrelated atomic
configurations from extensive data sets with a non-standard neural network
workflow known as random network distillation (RND) for training
machine-learned inter-atomic potentials (MLPs). This method is coupled with a
DFT workflow wherein initial data is generated with cheaper classical methods
before only the minimal subset is passed to a more computationally expensive ab
initio calculation. This benefits training not only by reducing the number of
expensive DFT calculations required but also by providing a pathway to the use
of more accurate quantum mechanical calculations for training. The method's
efficacy is demonstrated by constructing machine-learned inter-atomic
potentials for the molten salts KCl and NaCl. Our RND method allows accurate
models to be fit on minimal data sets, as small as 32 configurations, reducing
the required structures by at least one order of magnitude compared to
alternative methods.
