用块级生成提升大模型对金属有机框架的三维结构预测能力
Enhancing Spatial Reasoning in Large Language Models for Metal-Organic Frameworks Structure Prediction
- 将MOF结构分解为模块块，用大模型分块生成
- 在Qwen-3 8B上实现35.78%的结构匹配率，每结构生成仅需0.04秒
- 适合材料设计与生成式人工智能交叉领域的研究者
金属-有机框架（MOFs）是一类多孔晶态材料，在碳捕集和药物递送等领域有广泛应用，但其三维结构的准确预测仍是重大挑战。尽管大语言模型（LLMs）在晶体结构生成方面展现出潜力，但应用于MOFs时受限于其单元胞中大量原子带来的高复杂性。受深度生成模型中分块范式的启发，我们首次提出专为块级MOF结构预测设计的MOF-LLM框架。为有效利用LLMs完成这一三维模块化组装任务，训练范式融合了空间感知的持续预训练（CPT）、结构监督微调（SFT）与匹配驱动强化学习（RL）。通过引入显式空间先验，并借助软自适应策略优化（SAPO）提升结构稳定性，该方法显著增强了Qwen-3 8B模型的空间推理能力。综合实验表明，MOF-LLM达到35.78%的匹配率，且采样效率高达每结构0.04秒，性能优于现有方法。
原文摘要 · Abstract (English)
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 this domain by introducing MOF-LLM, the first LLM framework specifically adapted for block-level MOF structure prediction. To effectively harness LLMs for this 3D 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 in a Qwen-3 8B model for MOF structure prediction. Comprehensive experiments demonstrate that MOF-LLM achieves state-of-the-art performance with a match rate of 35.78% while exhibiting superior sampling efficiency of 0.04 seconds per structure.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。