Paving the road towards automated homogeneous catalyst design

Area of Science:

• Catalysis
• Computational Chemistry
• Materials Science

Background:

• Computational tools are essential for modern catalyst design and organic synthesis.
• Machine learning (ML) approaches are increasingly important for their advanced capabilities.
• High-throughput in silico methods offer valuable insights but require optimization.

Purpose of the Study:

• To provide an overview of computational catalyst design initiatives.
• To introduce automated tools for high-throughput in silico chemical space exploration.
• To highlight the importance of automation and modularity in computational workflows.

Main Methods:

• Review of diverse computational catalyst design strategies.
• Development and application of automated tools for in silico exploration.
• Integration of data-driven approaches with automated, modular workflows.

Main Results:

• Demonstration of automated tools for efficient chemical space exploration.
• Identification of key factors (automation, modularity) for enhancing computational methods.
• Framework for scaling homogeneous catalyst design through integrated workflows.

Conclusions:

• Automated, data-driven, and modular workflows are critical for advancing homogeneous catalyst design.
• Integration of these approaches enables unprecedented scale in catalysis research.
• Computational methods, particularly ML, significantly support experimental catalysis.