LLMs Predict Scalability of Metal-Organic Framework Syntheses
Researchers have developed ESU-MOF, a dataset and machine learning strategy that fine-tunes large language models to predict the scalability potential of metal-organic framework (MOF) syntheses with 91.4% accuracy. The work addresses the bottleneck between MOF discovery and industrial deployment by mining fragmented literature. The positive-unlabeled learning approach enables rapid data-driven triage for industrial MOF discovery. The study is published on arXiv (ID 2604.20899) under the Condensed Matter > Materials Science category.
Key facts
- ESU-MOF is a literature-mined dataset for MOF scalability.
- Fine-tuned large language models achieve 91.4% accuracy.
- Positive-unlabeled learning strategy is used.
- Scalable synthesis is a gate between MOF discovery and industrial deployment.
- Scale-up know-how is fragmented across reports.
- The work enables data-driven triage for industrial MOF discovery.
- Published on arXiv with ID 2604.20899.
- Categorized under Condensed Matter > Materials Science.
Entities
Institutions
- arXiv