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

## TL;DR

This paper introduces a largely automated method for building machine learning interatomic potentials that explore and fit potential-energy surfaces de novo, using a configuration-averaged kernel metric to select relevant structures efficiently.

## Contribution

The authors develop a protocol that automates the creation of ML potentials from scratch, reducing manual intervention and enabling broader application in materials science.

## Key findings

- Accurate potentials for diverse materials achieved with few DFT calculations.
- Automated structure selection improves robustness and efficiency.
- Method applicable to various chemical environments and coordination states.

## Abstract

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.