MatterChat: A Multi-Modal LLM for Material Science

• URL: http://arxiv.org/abs/2502.13107v1
• Date: Tue, 18 Feb 2025 18:19:36 GMT
• Title: MatterChat: A Multi-Modal LLM for Material Science
• Authors: Yingheng Tang, Wenbin Xu, Jie Cao, Jianzhu Ma, Weilu Gao, Steve Farrell, Benjamin Erichson, Michael W. Mahoney, Andy Nonaka, Zhi Yao,
• Abstract summary: We introduce MatterChat, a versatile structure-aware multi-modal large language model. We show that MatterChat significantly improves performance in material property prediction and human-AI interaction. We also demonstrate its usefulness in applications such as more advanced scientific reasoning and step-by-step material synthesis.
• Score: 40.34536331137755
• License:
• Abstract: 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.

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