Crystal Structure Prediction by Joint Equivariant Diffusion
Tsinghua University（清华大学）；Institute for AI, Tsinghua University（清华大学人工智能研究院）；Institute for AIR, Tsinghua University（清华大学AIR研究院）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；National Supercomputer Center in Guangzhou（广州国家超级计算中心）；School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；Stanford University（斯坦福大学）
Rui Jiao, Wenbing Huang, Peijia Lin, Jiaqi Han, Pin Chen, Yutong Lu, Yang Liu
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 coordinates for each crystal by employing a periodic-E(3)-equivariant denoising model, to better model the crystal geometry. Notably, different from related equivariant generative approaches, DiffCSP leverages fractional coordinates other than Cartesian coordinates to represent crystals, remarkably promoting the diffusion and the generation process of atom positions. Extensive experiments verify that our DiffCSP significantly outperforms existing CSP methods, with a much lower computation cost in contrast to DFT-based methods. Moreover, the superiority of DiffCSP is also observed when it is extended for ab initio crystal generation.
Equivariant Diffusion for Crystal Structure Prediction
School of Computer Science；Engineering, Sun Yat-sen University, Guangzhou, China；National Supercomputer Center in Guangzhou, China；Dept. of Comp. Sci. \& Tech., Institute for AI, Tsinghua University, Beijing, China；Institute for AIR, Tsinghua University, Beijing, China；Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China；Beijing Key Laboratory of Big Data Management；Analysis Methods, Beijing, China
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Max Planck Institute for Gravitational Physics（马克斯·普朗克引力物理研究所）；University of Tübingen（图宾根大学）
Department of Materials Science & Engineering, University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校材料科学与工程系）；Department of Computer Science, Princeton University（普林斯顿大学计算机科学系）；Materials Research Laboratory, University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校材料研究实验室）
An Efficient High-Degree, High-Order Equivariant Graph Neural Network for Direct Crystal Structure Optimization
Jinan University（暨南大学）；Nanyang Technological University（南洋理工大学）；National University of Singapore（新加坡国立大学）；National University of Singapore (Chongqing) Research Institute（新加坡国立大学（重庆）研究院）
Facet: highly efficient E(3)-equivariant networks for interatomic potentials
Department of Computer Science and Engineering, University of South Carolina（南卡罗来纳大学计算机科学与工程系）；Computational Science and Engineering, Georgia Institute of Technology（佐治亚理工学院计算科学与工程系）
Transformer-Enhanced Variational Autoencoder for Crystal Structure Prediction
Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）；vivo Mobile Communication Co., Ltd（维沃移动通信有限公司）；University of Chinese Academy of Sciences（中国科学院大学）
