Learning Collective Variables for Protein Folding through Physics-inspired Geodesic Interpolation
Leveraging physics-inspired geodesic interpolation, we propose an effective simulation-free data augmentation strategy to improve the learning of collective variables for enhanced sampling of protein folding.
