Catalyst gene profiling offers a promising approach for analyzing catalyst datasets by encoding catalyst data features into interpretable sequences known as catalyst genes. However, its practical adoption has been limited by technical complexity and the lack of accessible analytical tools. This study introduces the first web-based framework that integrates catalyst gene generation, hierarchical clustering, and symbolic similarity visualization within an interactive platform. Implemented in the CADS environment, the system supports simultaneous multi-view visualization and interactive analysis, enabling both global trend identification and localized feature recognition. By combining catalyst gene profiling with intuitive visual analytics, the framework lowers the barrier to entry for domain experts and facilitates exploratory research and rational catalyst design without requiring specialized computational expertise.
Shibata et al. (Thu,) studied this question.