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We introduce machine-learned potentials for Ag-Pd to describe the energy ofalloy configurations over a wide range of compositions. We compare twodifferent approaches. Moment tensor potentials (MTP) are polynomial-likefunctions of interatomic distances and angles. The Gaussian ApproximationPotential (GAP) framework uses kernel regression, and we use the Smooth Overlapof Atomic Positions (SOAP) representation of atomic neighbourhoods thatconsists of a complete set of rotational and permutational invariants providedby the power spectrum of the spherical Fourier transform of the neighbourdensity. Both types of potentials give excellent accuracy for a wide range ofcompositions and rival the accuracy of cluster expansion, a benchmark for thissystem. While both models are able to describe small deformations away from thelattice positions, SOAP-GAP excels at transferability as shown by sensibletransformation paths between configurations, and MTP allows, due to its lowercomputational cost, the calculation of compositional phase diagrams. Given thefact that both methods perform as well as cluster expansion would but yieldoff-lattice models, we expect them to open new avenues in computationalmaterials modeling for alloys.

Machine-learned Interatomic Potentials for Alloys and Alloy Phase Diagrams