Crystal structure generation with autoregressive large language modeling
University of Reading · University College London
Abstract
The generation of plausible crystal structures is often the first step in predicting the structure and properties of a material from its chemical composition. However, most current methods for crystal structure prediction are computationally expensive, slowing the pace of innovation. Seeding structure prediction algorithms with quality generated candidates can overcome a major bottleneck. Here, we introduce CrystaLLM, a methodology for the versatile generation of crystal structures, based on the autoregressive large language modeling (LLM) of the Crystallographic Information File (CIF) format. Trained on millions of CIF files, CrystaLLM focuses on modeling crystal structures through text. CrystaLLM can produce…
Citation impact
- FWCI
- 12.72
- Percentile
- 100%
- References
- 71
Authors
3Topics & keywords
- Bottleneck
- Computer science
- Crystal (programming language)
- Crystal structure prediction
- Autoregressive model
- Crystal structure
- Artificial intelligence
- Algorithm
- Industry, innovation and infrastructure