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

Peter Walther; Hongrui Sheng; Xinxin Liu; Bin Feng; Reid A. Coyle; Xinhua Yan; Kyle Smith; Harrison Kayal; Shyam Chand Pal; Zhiling Zheng

doi:10.48550/arxiv.2604.20899

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

arXiv (Cornell University) 2026 Preprint Open access

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Authors: Peter Walther, Hongrui Sheng, Xinxin Liu, Bin Feng, Reid A. Coyle, Xinhua Yan, Kyle Smith, Harrison Kayal, Shyam Chand Pal, Zhiling Zheng

DOI: 10.48550/arxiv.2604.20899

Cited by: 1

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.

Fields of Study & Subjects

Metal-Organic Frameworks: Synthesis and Applications Machine Learning in Materials Science Electrocatalysts for Energy Conversion

Publication details

Journal / source: arXiv (Cornell University)
Publication date: 21 April 2026
Document type: Preprint