Predicting Scale-Up of Metal-Organic Framework Syntheses with Large Language Models

• URL: http://arxiv.org/abs/2604.20899v1
• Date: Tue, 21 Apr 2026 14:59:01 GMT
• Title: Predicting Scale-Up of Metal-Organic Framework Syntheses with Large Language Models
• Abstract summary: 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.
• Score: 3.042408245033256
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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