Functional and Material Properties in Nanocatalyst Design: A Data Handling and Sharing Problem.

Area of Science:

• Chemistry
• Materials Science
• Computational Science

Background:

• Molecular representations include properties (functional/material) and descriptors.
• Functional properties (e.g., drug activity) have extensive databases, unlike catalyst data.
• Catalyst data primarily consists of calculated descriptors or material properties.

Purpose of the Study:

• To investigate the reasons behind the scarcity of real-world catalyst performance data.
• To identify challenges in data handling and sharing for catalyst design and discovery.

Main Methods:

• Review of data handling and sharing practices in catalyst design.
• Analysis of material informatics, structural coding, and data validation in catalyst research.
• Examination of infrastructure and online databases for catalyst design.

Main Results:

• Significant challenges exist in data collection, validation, and infrastructure for catalyst design.
• A severe deficit or "famine" of functional property data for catalysts is observed.
• Current catalyst databases predominantly contain calculated descriptors, not real-world performance metrics.

Conclusions:

• Accurate material design necessitates reliable real-world property measurements for prediction.
• The lack of accessible, measured catalyst performance data impedes scientific progress.
• Bridging the data gap is essential for effective catalyst discovery and development.