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

2026/04/21 by Peter Walther, Hongrui Sheng, Xinxin Liu +7 · 1 voice
Computer Science · Physics and Astronomy · #cond-mat.mtrl-sci #cs.AI

paper · pdf · doi:10.48550/arxiv.2604.20899

arxiv published 2026/04/21 · arxiv updated 2026/07/09

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.

Cited by

• Sustainable Metal-Organic Framework Water Harvesters in the Artificial Intelligence Era

Discussions

• Predicting Scale-Up of Metal-Organic Framework Syntheses with Large Language Models https://arxiv.org/abs/2604.20899 [bsky, 1 points, 0 comments]