  We introduce machine-learned potentials for Ag-Pd to describe the energy of
alloy configurations over a wide range of compositions. We compare two
different approaches. Moment tensor potentials (MTP) are polynomial-like
functions of interatomic distances and angles. The Gaussian Approximation
Potential (GAP) framework uses kernel regression, and we use the Smooth Overlap
of Atomic Positions (SOAP) representation of atomic neighbourhoods that
consists of a complete set of rotational and permutational invariants provided
by the power spectrum of the spherical Fourier transform of the neighbour
density. Both types of potentials give excellent accuracy for a wide range of
compositions and rival the accuracy of cluster expansion, a benchmark for this
system. While both models are able to describe small deformations away from the
lattice positions, SOAP-GAP excels at transferability as shown by sensible
transformation paths between configurations, and MTP allows, due to its lower
computational cost, the calculation of compositional phase diagrams. Given the
fact that both methods perform as well as cluster expansion would but yield
off-lattice models, we expect them to open new avenues in computational
materials modeling for alloys.
