  Recent progress towards universal machine-learned interatomic potentials
holds considerable promise for materials discovery. Yet the accuracy of these
potentials for predicting phase stability may still be limited. In contrast,
cluster expansions provide accurate phase stability predictions but are
computationally demanding to parameterize from first principles, especially for
structures of low dimension or with a large number of components, such as
interfaces or multimetal catalysts. We overcome this trade-off via transfer
learning. Using Bayesian inference, we incorporate prior statistical knowledge
from machine-learned and physics-based potentials, enabling us to sample the
most informative configurations and to efficiently fit first-principles cluster
expansions. This algorithm is tested on Pt:Ni, showing robust convergence of
the mixing energies as a function of sample size with reduced statistical
fluctuations.
