A new web-based tool developed by researchers at Hokkaido University promises to simplify the challenging process of designing advanced catalysts, which are vital substances that speed up chemical reactions across modern industries. Published in Science and Technology of Advanced Materials: Methods, the tool provides researchers with an intuitive way to view and explore catalyst data, enabling them to identify patterns and relationships in complex datasets without requiring advanced programming or computational expertise.
The tool's importance stems from catalysts' critical role in numerous applications, from manufacturing household chemicals to generating clean energy and recycling waste. Designing new catalysts has traditionally been difficult because their performance depends on many interacting factors. This new approach addresses that challenge by making catalyst data more interpretable and accessible to researchers who may not have specialized computational backgrounds.
The system employs an approach called catalyst gene profiling, where catalysts are represented as symbolic sequences. This representation allows scientists to apply sequence-based analysis methods to design and improve catalysts more effectively. The web-based graphical interface offers interactive visualizations that help researchers explore complex catalyst datasets, identify global trends, and recognize local features. As Professor Keisuke Takahashi, who led the study, explains, "By visualizing both the relationships among catalysts and the underlying gene-based features, the platform makes catalyst design more interpretable, accessible, and efficient, bridging the gap between data-driven analysis and practical experimental insight."
Users can view catalysts clustered based on feature or sequence similarity and examine a heat map that reveals how catalyst gene sequences are calculated. The different visualizations are synchronized and update simultaneously when users zoom in or select catalyst groups. The research team plans to extend the tool's capabilities to work with other material science datasets and include predictive components that would allow researchers to investigate new ideas for high-performance materials. They are also working to improve collaborative features so multiple researchers can work together to explore and annotate datasets, fostering a community-oriented approach to material design.
The tool's development represents a significant step toward making advanced materials research more intuitive and impactful. By lowering the technical barriers to catalyst data analysis, it could accelerate innovation in fields dependent on catalytic processes. The full research paper is available at https://doi.org/10.1080/27660400.2025.2600689, and additional information about the journal can be found at https://www.tandfonline.com/STAM-M.
