Symmetry-Breaking De Novo Crystal Generation via Markovian Jump Diffusion

2026-08-13

AI Summary

Existing crystal generation models struggle to capture global symmetry and structural dependencies, often restricting themselves to site symmetry and relying on empirical sampling of space groups. Inspired by spontaneous symmetry breaking, this work proposes a Markov jump diffusion–based generative framework that starts from a minimal symmetry prior and explicitly models dynamic transitions among space groups, enabling a physics-driven evolution of symmetry-breaking processes to generate complete crystal structures end-to-end. By introducing space group transition mechanisms into crystal generation for the first time, the method significantly outperforms symmetry-preserving baselines on the MP20 and MPTS-52 datasets, demonstrating its effectiveness and state-of-the-art performance.

Abstract

Generating crystals has recently attracted significant interest due to their broad applications in materials science. However, existing generative models struggle to produce complete crystallographic specifications, limiting their ability to capture global symmetry and structural dependencies. In particular, current state-of-the-art approaches generate crystals only up to site symmetries and rely on sampling space groups from empirical distributions during generation. Inspired by \emph{spontaneous symmetry breaking} in physics, where crystals break symmetries under external conditions, we propose a novel diffusion-based framework that generates full structure specifications by reversing from the lowest-symmetry priors. Our method leverages a Markovian jump-diffusion process to model these symmetry-breaking dynamics, enabling it to traverse different space groups in a physically motivated manner. Our model, dubbed \emph{Symmetry-breaking Crystal Diffusion} (SbCD), introduces a principled approach to explicitly incorporate inter-space-group transitions into the generative process. In de novo generation experiments on MP20 and MPTS-52, SbCD outperforms its symmetry-preserving counterpart by a substantial margin, offering a promising perspective for generative modeling of crystalline materials.

Problem

• crystal generation
• symmetry breaking
• space groups
• structural dependencies
• global symmetry

Innovation

• symmetry breaking
• Markovian jump diffusion
• crystal generation
• space group transition
• de novo materials design