machine-learning-interatomic-potentials

This paper proposes using atom-averaged features from pretrained MLIPs like MACE as coarse coordinates for evaluating and guiding inorganic crystal structure generation, introducing the Coarse-Fine Transport Distance (CFTD) metric that captures both quality and novelty in a distribution-based framework.