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

ABSTRACT Solid direct air capture (S‐DAC) technologies have the potential to remove pre‐existing CO 2 from the atmosphere by taking advantage of hydrophobic materials that will retain CO 2 through physisorption and separating it from the abundant water molecules. Calix[ n ]arenes are highly tunable and versatile complexes with inherent hydrophobic cavities, making them promising candidates for S‐DAC technologies. In this study, we investigated four classes of calix[4]arenes, which were functionalized symmetrically at various sites, using machine learning workflows based on molecular topology. The Topology‐Guided Carbon Capture with Calix[4]arenes (TopoC 3 ) model presented here leverages DFT data from an initial molecular subset together with persistent homology–based topological descriptors to enable targeted molecular discovery. Recognizing the strong dependence of CO 2 host‐guest interactions on molecular structure, this study identified key structural and topological features that govern adsorption performance. These insights establish clear design criteria for calix[4]arenes as molecular candidates for CO 2 capture and provide a principled framework for further molecular engineering.