5 papers
SLayerGen: a Crystal Generative Model for all Space and Layer Groups
Rees Chang, Andrew Novick, Ryan P Adams +1
Crystal generative models have shown rapid progress for accelerating the discovery of bulk, periodic materials. However, many material systems such as 2D superconductors, thin film…
A Single Architecture for Representing Invariance Under Any Space Group
Cindy Y. Zhang, Elif Ertekin, Peter Orbanz +1
Incorporating known symmetries in data into machine learning models has consistently improved predictive accuracy, robustness, and generalization. However, achieving exact invarian…
Space Group Equivariant Crystal Diffusion
Rees Chang, Angela Pak, Alex Guerra +4
Accelerating inverse design of crystalline materials with generative models has significant implications for a range of technologies. Unlike other atomic systems, 3D crystals are i…
Expressivity of determinantal ansatzes for neural network wave functions
Ni Zhan, William A. Wheeler, Gil Goldshlager +3
Neural network wave functions have shown promise as a way to achieve high accuracy on the many-body quantum problem. These wave functions most commonly use a determinant or sum of…
Diagonal Symmetrization of Neural Network Solvers for the Many-Electron Schrödinger Equation
Kevin Han Huang, Ni Zhan, Elif Ertekin +2
Incorporating group symmetries into neural networks has been a cornerstone of success in many AI-for-science applications. Diagonal groups of isometries, which describe the invaria…