@inproceedings{
yan2024invariant,
title={Invariant Tokenization of Crystalline Materials for Language Model Enabled Generation},
author={Keqiang Yan and Xiner Li and Hongyi Ling and Kenna Ashen and Carl Edwards and Raymundo Arroyave and Marinka Zitnik and Heng Ji and Xiaofeng Qian and Xiaoning Qian and Shuiwang Ji},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=18FGRNd0wZ}
}
We consider the problem of crystal materials generation using language models (LMs). A key step is to convert 3D crystal structures into 1D sequences to be processed by LMs. Prior studies used the crystallographic information framework (CIF) file stream, which fails to ensure SE(3) and periodic invariance and may not lead to unique sequence representations for a given crystal structure. Here, we propose a novel method, known as Mat2Seq, to tackle this challenge. Mat2Seq converts 3D crystal structures into 1D sequences and ensures that different mathematical descriptions of the same crystal are represented in a single unique sequence, thereby provably achieving SE(3) and periodic invariance. Experimental results show that, with language models, Mat2Seq achieves promising performance in crystal structure generation as compared with prior methods.
Invariant Tokenization of Crystalline Materials for Language Model Enabled Generation
Keqiang Yan Xiner Li Hongyi Ling Kenna Ashen Carl Edwards Raymundo Arroyave Marinka Zitnik Heng Ji Xiaofeng Qian Xiaoning Qian Shuiwang Ji