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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2601.09285 |
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| _version_ | 1866908764080177152 |
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| author | Pan, Mianzhi Li, JianFei Liu, Peishuo Wang, Botian Ouyang, Yawen Rong, Yiming Zhou, Hao Zhang, Jianbing |
| author_facet | Pan, Mianzhi Li, JianFei Liu, Peishuo Wang, Botian Ouyang, Yawen Rong, Yiming Zhou, Hao Zhang, Jianbing |
| contents | 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 crystals, their application to MOFs is hindered by MOFs' high atomic complexity. Inspired by the success of block-wise paradigms in deep generative models, we pioneer the use of LLMs in this domain by introducing MOF-LLM, the first LLM framework specifically adapted for block-level MOF structure prediction. To effectively harness LLMs for this modular assembly task, our training paradigm integrates spatial-aware continual pre-training (CPT), structural supervised fine-tuning (SFT), and matching-driven reinforcement learning (RL). By incorporating explicit spatial priors and optimizing structural stability via Soft Adaptive Policy Optimization (SAPO), our approach substantially enhances the spatial reasoning capability of a Qwen-3 8B model for accurate MOF structure prediction. Comprehensive experiments demonstrate that MOF-LLM outperforms state-of-the-art denoising-based and LLM-based methods while exhibiting superior sampling efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_09285 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Enhancing Spatial Reasoning in Large Language Models for Metal-Organic Frameworks Structure Prediction Pan, Mianzhi Li, JianFei Liu, Peishuo Wang, Botian Ouyang, Yawen Rong, Yiming Zhou, Hao Zhang, Jianbing Machine Learning Materials Science 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 crystals, their application to MOFs is hindered by MOFs' high atomic complexity. Inspired by the success of block-wise paradigms in deep generative models, we pioneer the use of LLMs in this domain by introducing MOF-LLM, the first LLM framework specifically adapted for block-level MOF structure prediction. To effectively harness LLMs for this modular assembly task, our training paradigm integrates spatial-aware continual pre-training (CPT), structural supervised fine-tuning (SFT), and matching-driven reinforcement learning (RL). By incorporating explicit spatial priors and optimizing structural stability via Soft Adaptive Policy Optimization (SAPO), our approach substantially enhances the spatial reasoning capability of a Qwen-3 8B model for accurate MOF structure prediction. Comprehensive experiments demonstrate that MOF-LLM outperforms state-of-the-art denoising-based and LLM-based methods while exhibiting superior sampling efficiency. |
| title | Enhancing Spatial Reasoning in Large Language Models for Metal-Organic Frameworks Structure Prediction |
| topic | Machine Learning Materials Science |
| url | https://arxiv.org/abs/2601.09285 |