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Unlocking broad downstream generalization for 3D atomistic systems, Atom-JEPA achieves state-of-the-art property prediction by learning from unlabeled structures alone.
Ensemble consensus training not only boosts predictive accuracy in molecular graphs but also allows individual models to outperform traditional ensembles, reshaping our approach to semi-supervised learning in this domain.
