59.2MTRL-SCIApr 21
Predicting Scale-Up of Metal-Organic Framework Syntheses with Large Language ModelsPeter Walther, Hongrui Sheng, Xinxin Liu et al.
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.