Predicting Scale-Up of Metal-Organic Framework Syntheses with Large Language Models
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arXiv
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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866915950595407872 |
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| author | Walther, Peter Sheng, Hongrui Liu, Xinxin Feng, Bin Coyle, Reid Yan, Xinhua Smith, Kyle Kayal, Harrison Pal, Shyam Chand Zheng, Zhiling |
| author_facet | Walther, Peter Sheng, Hongrui Liu, Xinxin Feng, Bin Coyle, Reid Yan, Xinhua Smith, Kyle Kayal, Harrison Pal, Shyam Chand Zheng, Zhiling |
| contents | Scalable synthesis remains the gate between MOF discovery and industrial deployment, as scale-up know-how is fragmented across disparate reports. We introduce ESU-MOF, a literature-mined dataset and a positive-unlabeled learning strategy that fine-tunes large language models to predict scalability potential with 91.4% accuracy, enabling rapid data-driven triage for industrial MOF discovery. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_20899 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Predicting Scale-Up of Metal-Organic Framework Syntheses with Large Language Models Walther, Peter Sheng, Hongrui Liu, Xinxin Feng, Bin Coyle, Reid Yan, Xinhua Smith, Kyle Kayal, Harrison Pal, Shyam Chand Zheng, Zhiling Materials Science Artificial Intelligence Scalable synthesis remains the gate between MOF discovery and industrial deployment, as scale-up know-how is fragmented across disparate reports. We introduce ESU-MOF, a literature-mined dataset and a positive-unlabeled learning strategy that fine-tunes large language models to predict scalability potential with 91.4% accuracy, enabling rapid data-driven triage for industrial MOF discovery. |
| title | Predicting Scale-Up of Metal-Organic Framework Syntheses with Large Language Models |
| topic | Materials Science Artificial Intelligence |
| url | https://arxiv.org/abs/2604.20899 |