Interpretable Machine Learning Disentangles Pore Confinement Across Multiple Zr-MOF CO2 Capture Targets
Year: 2026
Type: preprint
Abstract: Pore confinement in zirconium-based metal–organic frameworks (Zr-MOFs) is often described using the cage–window ratio (ΓCW), the largest cavity diameter divided by the pore-limiting diameter (Df), yet whether this composite coordinate carries information beyond Df alone remains unclear. We modeled CO2 uptake, heat of adsorption (HoA), and CO2/N2 selectivity for 239 Zr-containing MOFs at two pressures relevant to vacuum-swing adsorption using structure-grouped machine learning with SHAP interpretation, reaching test R2 values of 0.77–0.88. Although ΓCW correlated positively with all three targets, controlling for Df weakened these associations and reversed their sign, while the reciprocal analysis left the Df associations largely unchanged and a difference-based formulation produced no significant association. Df played different roles across the three targets, indicating that the apparent confinement effect mainly reflects the limiting aperture rather than relative cage–window architecture.
Source: ChemRxiv
Authors: Leyli Vali, Seyed Majid Hashemianzadeh
Institution: Iran University of Science and Technology
Language: English
Subfield: Inorganic Chemistry
Field: Chemistry
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7211946230