  The performance of machine learning interatomic potentials relies on the
quality of the training dataset. In this work, we present an approach for
generating diverse and representative training data points which initiates with
\it{ab initio} calculations for bulk structures. The data generation and
potential construction further proceed side-by-side in a cyclic process of
training the neural network and crystal structure prediction based on the
developed interatomic potentials. All steps of the data generation and
potential development are performed with minimal human intervention. We show
the reliability of our approach by assessing the performance of neural network
potentials developed for two inorganic systems.
