用大模型融合多种材料信息，提升性能预测准确率
LLM-Fusion: A Novel Multimodal Fusion Model for Accelerated Material Discovery
- 用大语言模型整合分子式、文本描述等多模态数据
- 在5个任务中优于单一模态和简单拼接方法
- 适合材料设计与加速发现的研究者使用
高效发现具有理想性质的材料仍是材料科学中的关键挑战。许多研究利用材料的不同信息集合来应对这一问题，其中多模态方法因其能融合多种信息源而展现出潜力。然而，现有融合算法仍较简单，缺乏对多模态丰富表征的支持。本文提出LLM-Fusion，一种新型多模态融合模型，利用大语言模型（LLMs）整合SMILES、SELFIES、文本描述和分子指纹等多种表示，实现更精准的材料性质预测。该方法采用灵活的基于LLM的架构，支持多模态输入处理，可在两个数据集上完成五个预测任务，结果表明其性能显著优于传统方法及单模态与简单拼接基线。
原文摘要 · Abstract (English)
Discovering materials with desirable properties in an efficient way remains a significant problem in materials science. Many studies have tackled this problem by using different sets of information available about the materials. Among them, multimodal approaches have been found to be promising because of their ability to combine different sources of information. However, fusion algorithms to date remain simple, lacking a mechanism to provide a rich representation of multiple modalities. This paper presents LLM-Fusion, a novel multimodal fusion model that leverages large language models (LLMs) to integrate diverse representations, such as SMILES, SELFIES, text descriptions, and molecular fingerprints, for accurate property prediction. Our approach introduces a flexible LLM-based architecture that supports multimodal input processing and enables material property prediction with higher accuracy than traditional methods. We validate our model on two datasets across five prediction tasks and demonstrate its effectiveness compared to unimodal and naive concatenation baselines.
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