Atom-JEPA: Joint-Embedding Predictive Architecture for 3D Atomistic Systems

Excerpt

arXiv:2610.08400v1 Announce Type: new Abstract: Large-scale self-supervised pretraining has reshaped modern machine learning, substantially advancing the ability of language and vision models to generalize across downstream tasks. While deep learning has driven considerable progress in modeling atomistic systems in recent years, self-supervised pretraining in this domain has not yet achieved comparable downstream generalization. To address this, we introduce Atom-JEPA, a self-supervised pretrain