  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.
