  The training set of atomic configurations is key to the performance of any
Machine Learning Force Field (MLFF) and, as such, the training set selection
determines the applicability of the MLFF model for predictive molecular
simulations. However, most atomistic reference datasets are inhomogeneously
distributed across configurational space (CS), thus choosing the training set
randomly or according to the probability distribution of the data leads to
models whose accuracy is mainly defined by the most common close-to-equilibrium
configurations in the reference data. In this work, we combine unsupervised and
supervised ML methods to bypass the inherent bias of the data for common
configurations, effectively widening the applicability range of MLFF to the
fullest capabilities of the dataset. To achieve this goal, we first cluster the
CS into subregions similar in terms of geometry and energetics. We iteratively
test a given MLFF performance on each subregion and fill the training set of
the model with the representatives of the most inaccurate parts of the CS. The
proposed approach has been applied to a set of small organic molecules and
alanine tetrapeptide, demonstrating an up to two-fold decrease in the root mean
squared errors for force predictions of these molecules. This result holds for
both kernel-based methods (sGDML and GAP/SOAP models) and deep neural networks
(SchNet model). For the latter, the developed approach simultaneously improves
both energy and forces, bypassing the compromise to be made when employing
mixed energy/force loss functions.

Chemical Physics