Unified physio-thermodynamic descriptors via learned CO2 adsorption properties in metal-organic frameworks

The large design space of metal-organic frameworks (MOFs) has prompted the utilization of deep learning to drive material design. Nonetheless, the prediction of key thermodynamic properties, such as heat of adsorption ( $$\Delta {H}_{{\rm{ads}}}$$ ), remains largely unexplored for CO2 adsorption in MOFs. Herein, we present IsothermNet, a high-throughput graph neural network designed to estimate uptake and $$\Delta {H}_{{\rm{ads}}}$$ over 0–50 bars, enabling high-quality full isotherm reconstruction (PCC: 0.73–0.95 [uptake], 0.76–0.88 [ $$\Delta {H}_{{\rm{ads}}}$$ ]). We further bridged these adsorption properties to uptake behaviors (i.e., isotherm shapes/types) and structural information by performing detailed ablation studies to investigate the relative importance of local and global features in relation to predictive performance. This comparative analysis facilitated the discovery of a (1) physically-interpretable and (2) analytically-derived universal descriptor set capable of illustrating interdependencies between easily-computed, accessible textural information and extrinsic adsorption properties. When used cooperatively with IsothermNet, these descriptors enable efficient material screening, accelerating high-performance MOF discovery for CO2 capture.