A new web-based tool developed by researchers at Hokkaido University promises to simplify the process of designing catalysts, which are crucial for manufacturing chemicals, generating clean energy, and recycling waste. The tool, described in a paper published in Science and Technology of Advanced Materials: Methods, provides an intuitive graphical interface for exploring complex catalyst datasets, enabling researchers to identify patterns and relationships without needing advanced programming or computational skills.
Catalysts accelerate chemical reactions, but designing new ones is challenging due to the many interacting factors that affect their performance. The new tool leverages an approach called catalyst gene profiling, where catalysts are represented as symbolic sequences. This makes it easier for scientists to interpret data and apply sequence-based analysis methods to design and improve catalysts.
“The system enables researchers to explore complex catalyst datasets, identify global trends, and recognize local features - all without requiring advanced programming skills,” said Professor Keisuke Takahashi, who led the study. “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 together based on feature similarity or sequence similarity. The tool includes a heat map that shows how catalyst gene sequences are calculated. Different visualizations can be viewed side by side and are synchronized, so all update simultaneously when a user zooms in or selects a group of catalysts.
The team plans to extend the tool to work with other materials science datasets, broadening its application. They are also working to integrate modeling and editing strategies, which would allow researchers not only to explore existing catalysts but also to investigate new ideas for high-performance materials. Additionally, they aim to improve collaborative features so that several researchers can work together to explore and annotate datasets, fostering a community-oriented, data-driven approach to material design and discovery.
“Our goal is to make advanced materials research more intuitive, approachable, and impactful,” said Takahashi.
The tool represents a significant step toward democratizing data-driven catalyst design, potentially accelerating the development of materials for clean energy, environmental remediation, and industrial processes. The full study is available at https://doi.org/10.1080/27660400.2025.2600689.
