arXiv · 2604.20899
Predicting Scale-Up of Metal-Organic Framework Syntheses with Large Language Models
Abstract
Scalable synthesis remains the gate between MOF discovery and industrial deployment, as scale-up know-how is fragmented across disparate reports. We introduce ScaleMOF, a literature-mined dataset and a positive-unlabeled learning strategy that fine-tunes large language models. Achieving 93.5% accuracy, this proof-of-concept serves as a literature-grounded ranking tool prioritizing plausible scale-up candidates.
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Peter Walther, Hongrui Sheng, Xinxin Liu, Bin Feng, Reid Coyle, Xinhua Yan, Kyle Smith, Harrison Kayal, Shyam Chand Pal, Zhiling Zheng. 2026-04-21. Predicting Scale-Up of Metal-Organic Framework Syntheses with Large Language Models. https://arxiv.org/abs/2604.20899
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