Calix[4]arene Design for Enhanced Carbon Capture via Topological Learning.

Area of Science:

• Materials Science
• Chemical Engineering
• Computational Chemistry

Background:

• Solid direct air capture (S-DAC) utilizes hydrophobic materials to capture atmospheric CO2.
• Calixarenes, with their tunable hydrophobic cavities, are promising candidates for S-DAC.
• Understanding structure-adsorption relationships is crucial for designing efficient CO2 capture materials.

Purpose of the Study:

• To develop a machine learning model for discovering novel calixarene-based S-DAC materials.
• To identify key structural and topological features governing CO2 adsorption in calixarenes.
• To establish design principles for engineering calixarenes for enhanced carbon capture.

Main Methods:

• Investigated four classes of symmetrically functionalized calix[4]arenes.
• Employed machine learning workflows based on molecular topology.
• Utilized DFT data and persistent homology-based topological descriptors.
• Developed the Topology-Guided Carbon Capture with Calix[4]arenes (TopoC3) model.

Main Results:

• Identified key structural and topological features influencing CO2 adsorption performance.
• Demonstrated the model's ability to guide targeted molecular discovery.
• Established clear design criteria for calixarene candidates in CO2 capture.

Conclusions:

• The TopoC3 model provides a principled framework for molecular engineering of calixarenes for S-DAC.
• Insights gained facilitate the rational design of materials for efficient atmospheric CO2 removal.
• This approach accelerates the discovery of advanced materials for climate change mitigation.