Predicting Scale-Up of Metal-Organic Framework Syntheses with Large Language Models
Authors: Peter Walther, Hongrui Sheng, Xinxin Liu, Bin Feng, Reid Coyle, Xinhua Yan, Kyle Smith, Harrison Kayal, +2 more
Organizations: Department of Chemistry, Washington University, St. Louis, MO 63130, USA. · Department of Chemistry, Fudan University, Shanghai 200438, China. · Department of Computer and Information Science, University of Pennsylvania, PA 19104, USA. · Institute of Materials Science & Engineering, Washington University, St. Louis, MO 63130, USA. · College of Chemistry and Materials Science, Fujian Normal University, Fuzhou 350117, China.
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.