Enhancing Spatial Reasoning in Large Language Models for Metal-Organic Frameworks Structure Prediction
arXiv:2601.09285v2 Announce Type: replace Abstract: Metal-organic frameworks (MOFs) are porous crystalline materials with broad applications such as carbon capture and drug delivery, yet accurately predicting their 3D structures remains a significant challenge. While Large Language Models (LLMs) have shown promise in generating crystal structures, their application to MOFs is hindered by MOFs' high structural complexity arising from the large number of atoms in unit cell. Inspired by the success of block-wise paradigms in deep generative models for MOFs, we pioneer the application of LLMs in t
LLMs continue to demonstrate capabilities beyond initial expectations, and researchers are actively exploring their application in traditionally complex scientific domains like materials science, leveraging recent advancements in generative models.
Improving the prediction of complex material structures like MOFs with AI accelerates materials discovery for crucial applications such as carbon capture and drug delivery, impacting energy, environmental, and pharmaceutical sectors.
The ability to more accurately predict highly complex material structures using LLMs reduces experimental trial-and-error, streamlining the design and synthesis of advanced materials.
- · Materials scientists
- · Pharmaceutical industry
- · Carbon capture technology developers
- · AI researchers
- · Traditional materials synthesis methods
Accelerated discovery of novel MOFs with superior properties for specific industrial applications.
Reduced R&D costs and shortened time-to-market for products relying on advanced porous materials.
New classes of materials and applications become economically viable, driving innovation in diverse sectors like sustainable energy and environmental remediation.
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