Catalyst‐Specialized Chemical Language Model Based on Transformer Variational Autoencoder for Catalyst Design and Discovery

Catalyst design has increasingly benefited from advances in AI, particularly chemical language models (CLMs). However, catalysts occupy a distinct chemical space characterized by unique elemental compositions and structural complexity, which limits the applicability of standard CLMs, thereby reducing their utility for broad catalyst discovery. Here, we present CatTransVAE, a catalyst‐specialized CLM based on a transformer variational autoencoder (VAE). CatTransVAE is developed through pretraining on general compounds and subsequent fine‐tuning on diverse catalyst databases. A new template‐guided generation framework is introduced to support controlled catalyst design under structural constraints. CatTransVAE demonstrates promising generative performance across multiple catalyst classes and achieves high task validity in template‐guided generation. In addition, the proposed two‐level embeddings derived from the transformer and VAE components exhibit competitive predictive performance compared with large‐scale CLMs. The practical utility of the framework is demonstrated through a catalyst optimization workflow. These results suggest that CatTransVAE provides a useful catalyst‐specialized representation and generation framework for accelerating catalyst design and discovery.