用大模型预测金属有机框架合成放大可行性，准确率达93.5%。

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

• 基于文献挖掘构建数据集，采用正负样本学习策略微调大模型
• 在预测合成放大可行性上达到93.5%准确率，可筛选出高潜力候选方案
• 适合材料研发人员快速评估实验方案的可扩展性

可扩展合成仍是金属有机框架（MOF）从发现到工业应用的关键瓶颈，因放大经验分散于不同文献中。本文提出ScaleMOF，一个基于文献挖掘的数据集及正-未标记学习策略，用于微调大语言模型。该概念验证方法在预测合成放大可行性上达到93.5%准确率，可作为基于文献的排序工具，优先筛选出具有可行性的放大候选方案。

原文摘要 · Abstract (English)

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.

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