3DTMC-LLM: A 3D Geometry-Aware Large Language Model for Transition Metal Complexes.

Abstract

Large language models (LLMs) have demonstrated remarkable reasoning capabilities across various natural language tasks. However, comparable breakthroughs in scientific discovery remain limited, as understanding complex chemical and physical phenomena demands multidimensional representations that extend far beyond language alone. Transition metal complexes (TMCs) are a well-recognized paradigm, essential for the development of catalysts and functional materials. The exploration of their vast and intricate design space, characterized by diverse coordination geometries and topological structures, poses significant challenges within language-based representations interpretable by LLMs. To address this limitation, we introduce 3DTMC-LLM, the first multimodal LLM designed specifically for TMCs. 3DTMC-LLM achieves efficient alignment of structural and textual spaces through a pretrained 3D encoder trained on 12 million TMCs, combined with a lightweight single-token projection layer. In downstream tasks, including knowledge/description generation, property prediction, and the more challenging reactivity modeling, 3DTMC-LLM was benchmarked against state-of-the-art closed-source LLMs (e.g., GPT-5.2) as well as domain-specific machine learning models. It achieved competitive or improved performance on several tasks, particularly those with strong three-dimensional dependencies. This framework highlights the potential of multimodal approaches to accelerate research in TMCs and suggests broader opportunities for advancing the development of the general-purpose chemistry model.