Space Group Informed Transformer for Crystalline Materials Generation
Zhendong Cao Xiaoshan Luo Jian Lv Lei Wang
We introduce CrystalFormer, a transformer-based autoregressive model
specifically designed for space group-controlled generation of crystalline
materials. The space group symmetry significantly simplifies the crystal space,
which is crucial for data and compute efficient generative modeling of
crystalline materials. Leveraging the prominent discrete and sequential nature
of the Wyckoff positions, CrystalFormer learns to generate crystals by directly
predicting the species and locations of symmetry-inequivalent atoms in the unit
cell. Our results demonstrate that CrystalFormer matches state-of-the-art
performance on standard benchmarks for both validity, novelty, and stability of
the generated crystalline materials. Our analysis also shows that CrystalFormer
ingests sensible solid-state chemistry information from data for generative
modeling. The CrystalFormer unifies symmetry-based structure search and
generative pre-training in the realm of crystalline materials. The simplicity,
generality, and flexibility of CrystalFormer position it as a promising
architecture to be the foundational model of the entire crystalline materials
space, heralding a new era in materials modeling and discovery.