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

Cluster expansion by transfer learning for phase stability predictions