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

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.