De novo exploration and self-guided learning of potential-energy surfaces

Understand

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.