ArticleNature communications2024
Crystal structure generation with autoregressive large language modeling.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
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- AI for Accelerated Materials Discovery: From Generative Design to Autonomous Realization.Chemical reviews · 2026Article
- Decoding the chemical space of fast-ion conductors via a descriptor-guided transfer learning framework.Science advances · 2026Article
- An exciting Approach to Theoretical Spectroscopy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Leveraging Large Language Models for Understanding Fundamental Principles of Catalysis.The journal of physical chemistry. C, Nanomaterials and interfaces · 2026Review
- Generative intelligence explores the chemical space of ten million catalysts.Chemical science · 2026Article
- Advancing battery failure diagnosis by knowledge-augmented large language models.National science review · 2026Article
- Fast and Flexible 3D Molecular Design Framework for Novel Organic Optoelectronic Materials.JACS Au · 2026Article
- Hydration control in molecular salts through counterion selection.Nature communications · 2026Article
- Are diffusion models ready for materials discovery in unexplored chemical space?Patterns (New York, N.Y.) · 2026Article
- A dual-mode large language model assistant for on-surface reactionsChemical science · 2026Article
- Improving crystal material property prediction with multi-view geometric graph transformer.Nature communications · 2026Article
- Generative modelling of inorganic materials with explicit electronic structure.Nature communications · 2026Article
- Benchmarking Large Language Models for Polymer Property Predictions.Macromolecular rapid communications · 2026Article
- Enhancing composition-based materials property prediction by cross-modal knowledge transfer.Scientific reports · 2026Article
- Design topological materials by reinforcement fine-tuned generative model.Nature communications · 2026Article
- Sustainable Materials Design With Multi-Modal Artificial Intelligence.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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 plausible crystal structures for a wide range of inorganic compounds unseen in training, as demonstrated by ab initio simulations. Our approach challenges conventional representations of crystals, and demonstrates the potential of LLMs for learning effective models of crystal chemistry, which will lead to accelerated discovery and innovation in materials science.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.