apo-unsupervised-atomic-policy-optimization-for-3d-structure-prediction-of-atomic-systems-442a8dd0·1 events·first seen Aliases: APO: Unsupervised Atomic Policy Optimization for 3D Structure Prediction of Atomic Systems
Researchers introduce Atomic Policy Optimization (APO), an unsupervised alignment framework for predicting 3D structures of atomic systems (crystals, antibodies) without requiring ground-truth coordinate labels. APO adapts group-relative policy optimization to 3D atomic environments using a dual-reward mechanism: an eigen-decomposition-based reward reinforcing dominant latent structural modes, and a thermodynamic stability reward. Benchmarks on crystal and antibody structure prediction show APO surpasses fully supervised baselines on match rates and structural fidelity while also improving inference efficiency by straightening probability paths. The work is significant for material science and drug discovery applications where experimental labels are scarce or prohibitively expensive.