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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.
A new dataset, MAD-1.5, promises to unlock more accurate and broadly applicable atomistic simulations by providing high-quality, consistent data across 102 elements of the periodic table.
