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CrystalREPA: Transferring Physical Priors from Universal MLIPs to Crystal Generative Models
Crystal generative models mainly learn what stable crystals look like, with little explicit supervision for what makes them stable. We reveal a substantial representation gap between state-of-the-art crystal generative models and pretrained universal machine learning interatomic potentials (MLIPs) v
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