Tackling the task of materials generation, we aim to enhance the previously proposed All-atom Diffusion Transformer (ADiT) by introducing SymADiT, a symmetry-aware variant. To do so, we use a representation of materials based on Wyckoff positions. We follow ADiT and perform generative modelling in latent space, adapted to our symmetry-aware representation. By forcing the output of the generative model to adhere to the symmetry restrictions imposed by the generated crystal's space group and each atom's Wyckoff-position, the generated materials exhibit more realistic symmetry properties. We benchmark our method against both symmetry-aware and symmetry-agnostic models for materials generation and show competitive performance, generating stable, symmetric materials with a simple Transformer architecture.
@article{arxiv.2605.10115,
title = {Generating Symmetric Materials using Latent Flow Matching},
author = {Anmar Karmush and Cedric Mathieu Brandenburg and Soheil Ershadrad and Johanna Rosén and Michael Felsberg and Filip Ekström Kelvinius},
journal= {arXiv preprint arXiv:2605.10115},
year = {2026}
}