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 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.
Advances in large language models are enabling their application to highly specialized scientific domains, accelerating discovery and practical implementation previously constrained by fragmented knowledge.
This development could significantly accelerate the transition of laboratory-scale scientific breakthroughs, particularly in materials science, to industrial production, impacting various sectors including energy and manufacturing.
The ability to predict the scalability of advanced material syntheses like MOFs using AI changes the development pipeline from empirical trial-and-error to a more data-driven, predictive approach.
- · Materials science industry
- · Chemical manufacturing
- · AI/ML companies
- · Deep tech investors
- · Traditional empiricist R&D methods
- · Companies slow to adopt AI in R&D
Faster and more efficient development of new advanced materials with industrial applications.
Increased competition and innovation in sectors reliant on new materials, leading to novel product categories and processes.
Enhanced material science capabilities could underpin advancements in adjacent fields like energy storage and carbon capture, accelerating their deployment.
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Read at arXiv cs.AI