Domain‐Trained Language Model for Inverse Design and Synthesis of High‐Performance Hydrogen Storage MOFs

Abstract A domain‐specific large language model, MOFs‐LLM, is developed to accelerate the inverse design and synthesis of metal—organic frameworks (MOFs) for hydrogen storage. Trained on 210 million tokens derived from over 6 000 MOF‐related publications and 15 000 crystal structures, the model integrates chemical knowledge with structural features to improve structure–property reasoning. Compared to baseline methods, MOFs‐LLM achieves a 46.7% enhancement in capturing structure–property relationships. It enables the inverse design of 60 candidate frameworks optimized for both hydrogen storage performance and synthetic accessibility. Guided by the model, a novel MOF (Cu‐LLMs‐1) was synthesized in three experimental iterations, exhibiting a hydrogen uptake of 1.33 wt% at room temperature, ranking among the top five pure MOFs under comparable conditions. These findings highlight the potential of domain‐trained language models to bridge virtual screening and experimental realization in materials discovery.