arXiv 2502.131072025-04-26cs.AIcs.LG

MatterChat：面向材料科学的多模态LLM

MatterChat: A Multi-Modal LLM for Material Science

Applied Mathematics and Computational Research Division, Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室应用数学与计算研究部）；National Energy Research Scientific Computing Center, Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室国家能源研究科学计算中心）；NSF National AI Institute for Student-AI Teaming, University of Colorado at Boulder（国家科学基金会学生-人工智能协同人工智能研究所，科罗拉多大学波德罗分校）；Department of Electrical and Computer Engineering, The University of Utah（犹他大学电气与计算机工程系）；Scientific Data Division, Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室科学数据部）；International Computer Science Institute, Berkeley, CA, USA（伯克利加州大学国际计算机科学研究所）；Department of Statistics, University of California at Berkeley（伯克利加州大学统计学系）

Yingheng Tang, Wenbin Xu, Jie Cao, Weilu Gao, Steve Farrell, Benjamin Erichson, Michael W. Mahoney, Andy Nonaka, Zhi Yao

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AI总结：

本文提出多模态大模型MatterChat，通过桥接模块将预训练原子间势与LLM对齐，统一材料结构与文本输入，显著提升了材料属性预测与人机交互性能。

摘要：

理解和预测无机材料的性质对于加速材料科学的发展以及推动能源、电子等领域的应用至关重要。通过多模态大型语言模型（LLMs）将材料结构数据与基于语言的信息相整合，具有通过增强人机交互来支持这些工作的巨大潜力。然而，一个关键挑战在于将全分辨率的原子结构整合到LLMs中。在这项工作中，我们引入了MatterChat，这是一种具备结构感知能力的通用多模态LLM，它将材料结构数据和文本输入统一到一个单一的内聚模型中。MatterChat采用桥接模块，将预训练的机器学习原子间势与预训练的LLM有效对齐，从而降低了训练成本并增强了灵活性。我们的结果表明，MatterChat在材料属性预测和人机交互方面显著提升了性能，超越了GPT-4等通用LLMs。我们还展示了其在更高级的科学推理和逐步材料合成等应用中的实用性。

英文摘要：

Understanding and predicting the properties of inorganic materials is crucial for accelerating advancements in materials science and driving applications in energy, electronics, and beyond. Integrating material structure data with language-based information through multi-modal large language models (LLMs) offers great potential to support these efforts by enhancing human-AI interaction. However, a key challenge lies in integrating atomic structures at full resolution into LLMs. In this work, we introduce MatterChat, a versatile structure-aware multi-modal LLM that unifies material structural data and textual inputs into a single cohesive model. MatterChat employs a bridging module to effectively align a pretrained machine learning interatomic potential with a pretrained LLM, reducing training costs and enhancing flexibility. Our results demonstrate that MatterChat significantly improves performance in material property prediction and human-AI interaction, surpassing general-purpose LLMs such as GPT-4. We also demonstrate its usefulness in applications such as more advanced scientific reasoning and step-by-step material synthesis.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。