Redirecting to original paper in 30 seconds...
Click below to go immediately or wait for automatic redirect
📄 Abstract
Abstract: Accelerating inverse design of crystalline materials with generative models
has significant implications for a range of technologies. Unlike other atomic
systems, 3D crystals are invariant to discrete groups of isometries called the
space groups. Crucially, these space group symmetries are known to heavily
influence materials properties. We propose SGEquiDiff, a crystal generative
model which naturally handles space group constraints with space group
invariant likelihoods. SGEquiD-iff consists of an SE(3)-invariant, telescoping
discrete sampler of crystal lattices; permutation-invariant, transformer-based
autoregressive sampling of Wyckoff positions, elements, and numbers of
symmetrically unique atoms; and space group equivariant diffusion of atomic
coordinates. We show that space group equivariant vector fields automatically
live in the tangent spaces of the Wyckoff positions. SGEquiDiff achieves
state-of-the-art performance on standard benchmark datasets as assessed by
quantitative proxy metrics and quantum mechanical calculations. Our code is
available at https://github.com/rees-c/sgequidiff.