In silico discovery of metal-organic frameworks for precombustion CO₂ capture using a genetic algorithm

Discovery of new adsorbent materials with a high CO₂ working capacity could help reduce CO₂ emissions from newly commissioned power plants using precombustion carbon capture. High-throughput computational screening efforts can accelerate the discovery of new adsorbents but sometimes require significant computational resources to explore the large space of possible materials. We report the in silico discovery of high-performing adsorbents for precombustion CO₂ capture by applying a genetic algorithm to efficiently search a large database of metal-organic frameworks (MOFs) for top candidates. High-performing MOFs identified from the in silico search were synthesized and activated and show a high CO₂ working capacity and a high CO₂/H₂ selectivity. One of the synthesized MOFs shows a higher CO₂ working capacity than any MOF reported in the literature under the operating conditions investigated here.