CatalyticMLLM: A Graph-Text Multimodal Large Language Model for Catalytic Materials

CatalyticMLLM is a newly proposed unified graph-text multimodal large language model designed for catalytic materials, integrating property prediction and inverse design in a shared representation space. The model demonstrates superior performance over decoupled baselines in both catalytic relaxed-energy prediction and structure generation tasks, enabling a closed-loop optimization workflow. This advancement is significant for practitioners as it addresses data distribution shifts and evaluator bias, enhancing the stability and reliability of catalytic material design processes.