Foundation Models meets Differentiable Physics

Find the post here

We introduced CLOUD, a scalable and physics-informed foundation model for crystal representation learning. CLOUD is pre-trained on over six million crystals using a symmetry-consistent string representation, and by integrating with a differentiable Debye model, it enables thermodynamically consistent, temperature-dependent prediction of phonon-related properties.