ArticleNature communications2025
Accurate prediction of synthesizability and precursors of 3D crystal structures via large language models.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Artificial intelligence-guided nanozyme engineering for chronic wound healing: from rational design to precision therapeutics.Bioactive materials · 2027Review
- Closed-Loop Solid-State Synthesis Planning for Materials Discovery With Large Language Models.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Leveraging Machine Learning for Accelerated Electrode-Electrolyte Interface Design in Rechargeable Li-Based Batteries.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Scaling multimodal materials representation learning with synthetic narratives.Nature communications · 2026Article
- Interrogating the synthetic likelihood of metal-organic frameworks: a digital discovery perspective.Chemical science · 2026Review
- Advancing battery failure diagnosis by knowledge-augmented large language models.National science review · 2026Article
- Sustainable Materials Design With Multi-Modal Artificial Intelligence.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Machine Learning for Designing Perovskites and Perovskite-Inspired Solar Materials: Emerging Opportunities and Challenges.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Generative Models for Crystalline Materials.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- A multimodal dataset of causal mechanisms in materials science literature.Scientific data · 2026Article
- Graphene-Inspired Mono-Elemental 2D Materials: Synthesis, Properties, and Potential Applications of Xenes.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Accurate prediction of synthesizability and precursors of 3D crystal structures via large language models.Nature communications · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Accessing the synthesizability of crystal structures is crucial for transforming theoretical materials into real-world applications. Nevertheless, there is a significant gap between actual synthesizability and thermodynamic or kinetic stability commonly used to screen synthesizable structures. Herein, we develop the Crystal Synthesis Large Language Models (CSLLM) framework, which utilizes three specialized LLMs to predict the synthesizability of arbitrary 3D crystal structures, possible synthetic methods, and suitable precursors, respectively. We construct a comprehensive dataset including synthesizable/non-synthesizable crystal structures and develop an efficient text representation for crystal structures to fine-tune LLMs. Our Synthesizability LLM achieves state-of-the-art accuracy (98.6%), significantly outperforming traditional synthesizability screening based on thermodynamic and kinetic stability. Its outstanding generalization ability is further demonstrated in experimental structures with complexity considerably exceeding that of the training data. Furthermore, both the Method and Precursor LLMs exceed 90% accuracy in classifying possible synthetic methods and identifying solid-state synthetic precursors for common binary and ternary compounds, respectively. Leveraging CSLLM, tens of thousands of synthesizable theoretical structures are successfully identified, with their 23 key properties predicted using accurate graph neural network models.
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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.