  The advent of machine learning in materials science opens the way for
exciting and ambitious simulations of large systems and long time scales with
the accuracy of ab-initio calculations. Recently, several pre-trained universal
machine learned interatomic potentials (UPMLIPs) have been published, i.e.,
potentials distributed with a single set of weights trained to target systems
across a very wide range of chemistries and atomic arrangements. These
potentials raise the hope of reducing the computational cost and methodological
complexity of performing simulations compared to models that require
for-purpose training. However, the application of these models needs critical
evaluation to assess their usability across material types and properties. In
this work, we investigate the application of the following UPMLIPs: MACE,
CHGNET, and M3GNET to the context of alloy theory. We calculate the mixing
enthalpies and volumes of 21 binary alloy systems and compare the results with
DFT calculations to assess the performance of these potentials over different
properties and types of materials. We find that small relative energies
necessary to correctly predict mixing energies are generally not reproduced by
these methods with sufficient accuracy to describe correct mixing behaviors.
However, the performance can be significantly improved by supplementing the
training data with relevant training data. The potentials can also be used to
partially accelerate these calculations by replacing the ab-initio structural
relaxation step.
