Despite their promise, these models often stumble by producing invalid or irrelevant molecular structures. Furthermore, fine-tuning and retraining these models demand substantial labeled data at times, complicated training procedures, intensive computational resources, and a significant amount of time, making the process costly and sometimes impractical for many tasks. Adding to these hurdles, acquiring training data for these models is challenging, as data must be sourced from disparate materials databases 8 with potentially different formats. These procedural issues further contribute to the challenges of using chemistry-specific generative models to transform raw data into actionable insights for materials discovery.