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Large language models predict MOF synthesis scale-up with 93.5% accuracy

By PulseAugur Editorial · [1 sources] ·

Researchers have developed ScaleMOF, a new dataset and learning strategy designed to predict the scalability of metal-organic framework (MOF) syntheses. By fine-tuning large language models, this approach aims to bridge the gap between MOF discovery and industrial application by identifying promising candidates for scale-up. The proof-of-concept system achieved 93.5% accuracy, serving as a literature-grounded tool for prioritizing MOF scale-up. AI

IMPACT This research demonstrates a novel application of LLMs in materials science, potentially accelerating the industrial adoption of newly discovered MOFs.

RANK_REASON The cluster describes a research paper detailing a new method for predicting material synthesis scale-up using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

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AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Large language models predict MOF synthesis scale-up with 93.5% accuracy

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The cluster describes a research paper detailing a new method for predicting material synthesis scale-up using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Peter Walther, Hongrui Sheng, Xinxin Liu, Bin Feng, Reid Coyle, Xinhua Yan, Kyle Smith, Harrison Kayal, Shyam Chand Pal, Zhiling Zheng ·

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

    arXiv:2604.20899v2 Announce Type: replace-cross 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 l…

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