Fast process-level screening of metal-organic frameworks for adsorption separation using 3D classical density functional theory.

Adsorption-based separation using metal-organic frameworks (MOFs) represents a promising, energy-efficient alternative to conventional separation technologies. While the vast design space of MOFs allows for precise property tuning to specific applications, the sheer scale of the design space renders exhaustive experimental testing impractical. Consequently, materials discovery relies on large-scale computational screening. Current screening workflows typically employ Grand-Canonical Monte Carlo (GCMC) simulations to predict adsorption properties; however, the high computational cost of GCMC often limits the number of materials screened. In this work, we introduce a process-level screening paradigm leveraging the computational speed of GPU-accelerated 3D classical density functional theory (cDFT). Validation against state-of-the-art GCMC simulations demonstrates that cDFT accurately predicts process key performance indicators (KPIs). The KPIs calculated via cDFT generally deviate by less than 5% from GCMC results while reducing computational costs by two to four orders of magnitude. Leveraging this increased efficiency, we screen the publicly available CoRE MOF 2025 database for methane/nitrogen separation using a temperature-swing adsorption process. By evaluating performance across a wide range of feed compositions, from industrial gas streams to dilute methane sources, the study identifies MOF candidates with robust performance profiles. The entire screening of over 5000 MOFs, requiring 460 000 adsorption calculations, was completed in only five days using two GPUs. The work establishes cDFT as a reliable, high-throughput approach for process-informed material discovery.