Crystal Structure Prediction by Joint Equivariant Diffusion

arXiv:2309.04475v2 Announce Type: replace-cross
Abstract: Crystal Structure Prediction (CSP) is crucial in various scientific disciplines. While CSP can be addressed by employing currently-prevailing generative models (e.g. diffusion models), this task encounters unique challenges owing to the symmetric geometry of crystal structures -- the invariance of translation, rotation, and periodicity. To incorporate the above symmetries, this paper proposes DiffCSP, a novel diffusion model to learn the structure distribution from stable crystals. To be specific, DiffCSP jointly generates the lattice and atom …

abstract arxiv challenges cond-mat.mtrl-sci cs.lg csp diffusion diffusion models generative generative models geometry prediction rotation translation type