# Efficient and Accurate Machine-Learning Interpolation of Atomic Energies in Compositions with Many Species

## TL;DR

This paper introduces a simple, efficient descriptor for machine-learning potentials that scales independently of the number of chemical species, enabling accurate simulations of complex materials with many elements.

## Contribution

The authors demonstrate that a constant-complexity descriptor suffices for accurate ML potentials in multi-species systems, overcoming previous quadratic scaling limitations.

## Key findings

- Achieved around 3 meV/atom accuracy for transition-metal oxides and biomolecules with 11 species.
- Proved that quadratic scaling of descriptors is unnecessary for complex compositions.
- Enabled ML simulations of previously inaccessible multi-species materials.

## Abstract

Machine-learning potentials (MLPs) for atomistic simulations are a promising alternative to conventional classical potentials. Current approaches rely on descriptors of the local atomic environment with dimensions that increase quadratically with the number of chemical species. In this article, we demonstrate that such a scaling can be avoided in practice. We show that a mathematically simple and computationally efficient descriptor with constant complexity is sufficient to represent transition-metal oxide compositions and biomolecules containing 11 chemical species with a precision of around 3 meV/atom. This insight removes a perceived bound on the utility of MLPs and paves the way to investigate the physics of previously inaccessible materials with more than ten chemical species.