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We present an automated procedure for computing stacking fault energies inrandom alloys from large-scale simulations using moment tensor potentials(MTPs) with the accuracy of density functional theory (DFT). To that end, wedevelop an algorithm for training MTPs on random alloys. In the first step, ouralgorithm constructs a set of ~10000 or more training candidate configurationswith 50-100 atoms that are representative for the atomic neighborhoodsoccurring in the large-scale simulation. In the second step, we use activelearning to reduce this set to ~100 most distinct configurations - for whichDFT energies and forces are computed and on which the potential is ultimatelytrained. We validate our algorithm for the MoNbTa medium-entropy alloy byshowing that the MTP reproduces the DFT $\frac{1}{4}[111]$ unstable stackingfault energy over the entire compositional space up to a few percent.  Contrary to state-of-the-art methods, e.g., the coherent potentialapproximation (CPA) or special quasi-random structures (SQSs), our algorithmnaturally accounts for relaxation, is not limited by DFT cell sizes, and opensopportunities to efficiently investigate follow-up problems, such as chemicalordering. In a broader sense, our algorithm can be easily modified to computerelated properties of random alloys, for instance, misfit volumes, or grainboundary energies. Moreover, it forms the basis for an efficient constructionof MTPs to be used in large-scale simulations of multicomponent systems.

Machine-learning potentials enable predictive $\textit{and}$ tractable high-throughput screening of random alloys