Supervised learning with deep models has tremendous potential for applications in materials science. Recently, graph neural networks have been used in this context, drawing direct inspiration from models for molecules. However, materials are typically much more structured than molecules, which is a feature that these models do not leverage. In this work, we introduce a class of models that are equivariant with respect to crystalline symmetry groups. We do this by defining a generalization of the message passing operations that can be used with more general permutation groups, or that can alternatively be seen as defining an expressive convolution operation on the crystal graph. Empirically, these models achieve competitive results with state-of-the-art on property prediction tasks.
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）
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
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）
