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

Yongchul G. Chung, Diego A. Gómez-Gualdrón, Peng Li, Karson T. Leperi, Pravas Deria, Hongda Zhang, Nicolaas A. Vermeulen, J. Fraser Stoddart, Fengqi You, Joseph T. Hupp, Omar K. Farha, Randall Q. Snurr*

*Corresponding author for this work

Research output: Contribution to journalArticle

79 Scopus citations

Abstract

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 highperforming 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.

Original languageEnglish (US)
Article numbere1600909
JournalScience Advances
Volume2
Issue number10
DOIs
StatePublished - Oct 2016

ASJC Scopus subject areas

  • General

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