Going beyond the Ordered Bulk: A Perspective on the Use of the Cambridge Structural Database for Predictive Materials...

Area of Science:

• Molecular Materials Science
• Crystallography
• Data Science

Background:

• The Cambridge Structural Database (CSD), founded in 1965, pioneered standardized scientific data collection.
• It has grown into an essential resource with over 1.25 million structures and advanced analytical software.
• The Cambridge Crystallographic Data Centre (CCDC) provides comprehensive tools for data management and analysis.

Purpose of the Study:

• To discuss the application of the CSD and CCDC tools for predictive materials design.
• To highlight the use of data mining and machine learning in materials science.
• To identify current and future challenges in materials design using CSD and CCDC capabilities.

Main Methods:

• Utilizing the Cambridge Structural Database (CSD) for structural data.
• Employing Cambridge Crystallographic Data Centre (CCDC) software for searching, visualization, and analysis.
• Applying data mining and machine learning techniques to materials design problems.

Main Results:

• Demonstrated the utility of CSD and CCDC tools in addressing multiscale challenges in materials design.
• Presented case studies from current research areas showcasing practical applications.
• Identified specific areas where existing or new CCDC capabilities can enhance materials design.

Conclusions:

• The CSD and CCDC tools are powerful resources for advancing predictive materials design.
• Data mining and machine learning integration offers significant potential for future materials discovery.
• Ongoing development of CCDC capabilities will further address complex challenges in molecular materials science.