(609g) GPU-Empowered High-Throughput Screening of Metal-Organic Frameworks for Efficient Membrane Separation of C2H4/C2H6

Ethene is one of the most important chemicals in the chemical industry. Conventional approaches for separating ethene from low molecular weight alkanes such as ethane are extremely energy-intensive. Metal-organic frameworks (MOFs) provide a promising alternative because of their tailorable pore size and excellent mechanic stability. In this work, we evaluate the performance of over 10,000 MOFs from the computational-ready, experimental (CoRE 2019) MOF database for C₂H₄/C₂H₆ separation with efficient theoretical methods to calculate the gas diffusivity and Henry’s constants empowered by parallel implementation with GPU. We identify MOFs with the best membrane selectivity and a structure-property relationship that can be utilized to design new materials with better performance.