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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月5日

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  • Joonhyeok Park,
  • Tao Li,
  • Yongjin Lee

摘要

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.