  The generation of plausible crystal structures is often the first step in
predicting the structure and properties of a material from its chemical
composition. Quickly generating and predicting inorganic crystal structures is
important for the discovery of new materials, which can target applications
such as energy or electronic devices. 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
plausible crystal structures for a wide range of inorganic compounds unseen in
training, as demonstrated by ab initio simulations. The integration with
predictors of formation energy permits the use of a Monte Carlo Tree Search
algorithm to improve the generation of meaningful structures. Our approach
challenges conventional representations of crystals, and demonstrates the
potential of LLMs for learning effective 'world models' of crystal chemistry,
which will lead to accelerated discovery and innovation in materials science.
