Enhancing Spatial Reasoning in Large Language Models for Metal-Organic Frameworks Structure Prediction

TL;DR

This paper introduces MOF-LLM, a novel large language model framework designed for block-level prediction of metal-organic frameworks' 3D structures, significantly improving spatial reasoning and prediction accuracy.

Contribution

It pioneers the adaptation of LLMs for MOF structure prediction using block-wise modeling and a specialized training paradigm with spatial priors and reinforcement learning.

Findings

MOF-LLM outperforms existing methods in accuracy.

Enhanced spatial reasoning in LLMs for MOFs.

Improved sampling efficiency over prior approaches.

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 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.