Catalysts informatics: paradigm shift towards data-driven catalyst design

Designing catalysts is a challenging matter as catalysts are involved with various factors that impact synthesis, catalysts, reactor and reaction. In order to overcome these difficulties, catalysts informatics is proposed as an alternative way to design and understand catalysts. The underlying concept of catalysts informatics is to design the catalysts from trends and patterns found in catalysts data. Here, three key concepts are introduced: experimental catalysts database, knowledge extraction from catalyst data via data science, and a catalysts informatics platform. Methane oxidation is chosen as a prototype reaction for demonstrating various aspects of catalysts informatics. This work summarizes how catalysts informatics plays a role in catalyst design. The work covers big data generation via high throughput experiments, machine learning, catalysts network method, catalyst design from small data, catalysts informatics platform, and the future of catalysts informatics via ontology. Thus, the proposed catalysts informatics would help innovate how catalysts can be designed and understood.

Automated summary

Designing catalysts is challenging due to the various factors involved. Catalysts informatics provides an alternative approach by designing catalysts based on trends and patterns found in catalysts data. This study introduces three key concepts: experimental catalysts database, knowledge extraction from data, and a catalysts informatics platform. The role of catalysts informatics is demonstrated through the example of methane oxidation. This work covers data generation, machine learning, catalysts network method, small data design, informatics platform, and the future of catalysts informatics.