Reac-Discovery: an artificial intelligence-driven platform for continuous-flow catalytic reactor discovery and...

Area of Science:

• Chemical Engineering
• Materials Science
• Computational Chemistry

Background:

• Traditional reactor engineering relies on human input for design.
• Digital technologies offer potential for novel reactor geometries and performance improvements.

Purpose of the Study:

• Introduce Reac-Discovery, a digital platform for integrated catalytic reactor design, fabrication, and optimization.
• Utilize periodic open-cell structures (POCs) for advanced reactor geometries.
• Demonstrate the platform's capability through case studies in catalysis.

Main Methods:

• Reac-Gen: Parametric design and analysis of structures using mathematical models.
• Reac-Fab: High-resolution 3D printing and functionalization of reactors.
• Reac-Eval: Self-driving laboratory with NMR monitoring and ML optimization.
• Printability validation algorithm for reactor designs.

Main Results:

• Reac-Discovery successfully integrated design, fabrication, and optimization.
• Demonstrated high-resolution 3D printing of complex catalytic reactors.
• Achieved highest reported space-time yield (STY) for triphasic CO₂ cycloaddition using immobilized catalysts.

Conclusions:

• Reac-Discovery represents a significant advancement in digitalizing catalytic reactor engineering.
• The platform enables rapid optimization of reactor performance through AI and automated experimentation.
• This integrated approach accelerates the discovery and implementation of efficient catalytic processes.