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Understanding and predicting the properties of inorganic materials is crucialfor accelerating advancements in materials science and driving applications inenergy, electronics, and beyond. Integrating material structure data withlanguage-based information through multi-modal large language models (LLMs)offers great potential to support these efforts by enhancing human-AIinteraction. However, a key challenge lies in integrating atomic structures atfull resolution into LLMs. In this work, we introduce MatterChat, a versatilestructure-aware multi-modal LLM that unifies material structural data andtextual inputs into a single cohesive model. MatterChat employs a bridgingmodule to effectively align a pretrained machine learning interatomic potentialwith a pretrained LLM, reducing training costs and enhancing flexibility. Ourresults demonstrate that MatterChat significantly improves performance inmaterial property prediction and human-AI interaction, surpassinggeneral-purpose LLMs such as GPT-4. We also demonstrate its usefulness inapplications such as more advanced scientific reasoning and step-by-stepmaterial synthesis.

MatterChat: A Multi-Modal LLM for Material Science