Few-Shot Ensemble Learning for Catalysis and Application to Trimetallics for Oxygen Reduction.

Machine-learned interatomic potentials (MLIPs) are increasingly used to accelerate catalyst discovery, but their accuracy and utility are often unclear, particularly when applying them to different computational setups or design spaces than those of the training data. This hinders their effective use in catalyst screening. Here, we improve accuracy and provide reliable uncertainty quantification through an ensemble-based, few-shot transfer learning strategy. The framework applies a bias-correction procedure to an ensemble of catalysis-focused MLIPs using a small number of density functional theory (DFT) labels from the target setup and design space. Applied to OH adsorption on bimetallic alloys, the approach reduces root mean squared errors (RMSEs) by 60% on average after incorporating just one additional DFT-calculated adsorption energy. For H adsorption on single-atom alloys, even the zero-shot ensemble is more accurate than any of the MLIPs, with further improvements when three to five DFT calculations are used for bias correction. The corrected ensemble yields well-calibrated uncertainty estimates, with miscalibration areas of just 0.039 and 0.088 for the two data sets. In a proof-of-concept screening campaign, the method identified a promising trimetallic candidate catalyst for the oxygen reduction reaction using only four total DFT-calculated adsorption energies. Taken together, these results demonstrate that few-shot bias correction enables reliable transfer of MLIP predictions across mismatched alloy search spaces and DFT methodologies, providing a practical route to accurate, uncertainty-aware catalyst screening with high efficiency.