Platonic representation of foundation machine learning interatomic potentials

We demonstrate that independently developed machine learning interatomic potentials exhibit consistent geometric organization of atomic environments. We unified the latent spaces of seven different models into a shared metric space using atomic anchors as reference points. This “Platonic representation” preserves chemical periodicity and structural properties, enabling cross-model comparison, interpretable embedding operations, and bias detection.