Crystalite: A Lightweight Transformer for Efficient Crystal Modeling

2026-04-02

AI Summary

This work addresses the inefficiency and limited structural modeling capacity of conventional crystal generation methods that rely on computationally expensive equivariant graph neural networks. The authors propose a lightweight diffusion Transformer that replaces high-dimensional one-hot encodings with subatomic tokenization and introduces a Geometry-Enhanced Module (GEM) to directly inject periodic geometric information from minimum-image pairs into the attention mechanism. This approach preserves the computational efficiency of standard Transformers while effectively integrating crystallographic chemistry and spatial symmetry. The method achieves state-of-the-art performance in both crystal structure prediction and ab initio generation tasks, attaining the highest S.U.N. discovery score and demonstrating significantly faster sampling speeds compared to existing geometry-intensive baselines.

Abstract

Generative models for crystalline materials often rely on equivariant graph neural networks, which capture geometric structure well but are costly to train and slow to sample. We present Crystalite, a lightweight diffusion Transformer for crystal modeling built around two simple inductive biases. The first is Subatomic Tokenization, a compact chemically structured atom representation that replaces high-dimensional one-hot encodings and is better suited to continuous diffusion. The second is the Geometry Enhancement Module (GEM), which injects periodic minimum-image pair geometry directly into attention through additive geometric biases. Together, these components preserve the simplicity and efficiency of a standard Transformer while making it better matched to the structure of crystalline materials. Crystalite achieves state-of-the-art results on crystal structure prediction benchmarks, and de novo generation performance, attaining the best S.U.N. discovery score among the evaluated baselines while sampling substantially faster than geometry-heavy alternatives.

Problem

• crystal modeling
• generative models
• equivariant graph neural networks
• sampling efficiency
• training cost

Innovation

• Subatomic Tokenization
• Geometry Enhancement Module
• Diffusion Transformer
• Crystal Structure Prediction
• De Novo Generation