PSYMOF: computational workflow enabling systematic post-synthesis modification of metal-organic frameworks

PSYMOF is a fully automated computational platform that introduces a new dimension in metal-organic framework (MOF) design by treating post-synthetic modification (PSM) as a tunable design variable. PSYMOF enables functional groups’ systematic and chemically feasible attachment at user-defined substitution levels across predefined bonding sites by integrating cheminformatics, a sterically aware random-walk growth algorithm, and molecular dynamics simulations. To demonstrate its capabilities, we present a comprehensive case study involving the partial functionalization of UiO-66-NH₂ with various functional groups for CO₂/N₂ separation. The simulation results reveal non-monotonic trends in adsorption performance, identifying optimal functionalization levels where enhanced CO₂ affinity balances pore accessibility to maximize separation efficiency. By enabling rapid exploration of the degree of functionalization—an often-overlooked yet critical factor in metal–organic framework (MOF) property tuning—PSYMOF serves as a powerful and generalizable tool for accelerating the discovery and optimization of functionalized porous materials tailored for gas adsorption and separation.