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

arXiv: https://arxiv.org/abs/2604.20899 · HTML: https://mlsanity.com/paper/2604.20899/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 7 references that are in the map.
- First posted on arXiv 21 Apr 2026.

## Built on by (most cited first, 1 of 1)

- [Sustainable Metal-Organic Framework Water Harvesters in the Artificial Intelligence Era](https://mlsanity.com/paper/2605.29179/) (2026)

## Abstract (arXiv)

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.