Physical consistency constraints alone can guide better 3D structure prediction than supervised coordinate matching, enabling practical structure modeling when ground-truth labels are unavailable.
APO is an unsupervised method for predicting 3D atomic structures (crystals, proteins) without needing labeled reference structures. It uses a dual-reward system—one that identifies physically realistic configurations through statistical analysis, another that enforces thermodynamic stability—allowing models to self-correct during training.