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Bridging the gap between simulation and reality, this work delivers machine learning potentials that are experimentally-validated, uncertainty-aware, and as accurate as the best electronic-structure methods.
Achieve up to 96% training time reduction for calibrated machine learning interatomic potentials by fine-tuning shallow ensembles, without sacrificing calibration quality.
