Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation

Generative machine learning is increasingly tested for inorganic materials generation. Most models and the corresponding evaluation approaches thereby rely on simple forms of crystal structure representations. In this paper, we showcase the power of integrating atom-averaged features from pretrained Machine-Learning Interatomic Potentials (MLIPs), such as MACE, into these tasks. We first introduce the Coarse-Fine Transport Distance (CFTD), a distance measure that captures both quality and novelty of materials created with generative models in a single distribution-based evaluation framework. It uses two different featurizers, directly deriving the quality component from coarse MACE representations. We showcase CFTD's versatility in capturing crystal structure quality while also detecting memorization and compare it with the recently introduced continuous SUN metrics. We further illustrate the power of MLIP representations by directly adding them to the generation process itself and show that coarse MACE features can be used as helpful guidance for a material generative model.