  Interatomic potential models based on machine learning (ML) are rapidly
developing as tools for materials simulations. However, because of their
flexibility, they require large fitting databases that are normally created
with substantial manual selection and tuning of reference configurations. Here,
we show that ML potentials can be built in a largely automated fashion,
exploring and fitting potential-energy surfaces from the beginning (de novo)
within one and the same protocol. The key enabling step is the use of a
configuration-averaged kernel metric that allows one to select the few most
relevant structures at each step. The resulting potentials are accurate and
robust for the wide range of configurations that occur during structure
searching, despite only requiring a relatively small number of single-point DFT
calculations on small unit cells. We apply the method to materials with diverse
chemical nature and coordination environments, marking a milestone toward the
more routine application of ML potentials in physics, chemistry, and materials
science.
