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Title: In silico discovery of metal-organic frameworks for precombustion CO 2 capture using a genetic algorithm

Discovery of new adsorbent materials with a high CO 2 working capacity could help reduce CO 2 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. Here in this paper we report the in silico discovery of high-performing adsorbents for precombustion CO 2 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 CO 2 working capacity and a high CO 2/H 2 selectivity. One of the synthesized MOFs shows a higher CO 2 working capacity than any MOF reported in the literature under the operating conditions investigated here.
Authors:
 [1] ;  [2] ;  [3] ;  [1] ;  [3] ;  [1] ;  [3] ;  [3] ;  [1] ;  [3] ;  [4] ;  [1]
  1. Northwestern Univ., Evanston, IL (United States). Dept. of Chemical and Biological Engineering
  2. Northwestern Univ., Evanston, IL (United States). Dept. of Chemical and Biological Engineering; Colorado School of Mines, Golden, CO (United States). Dept. of Chemical and Biological Engineering
  3. Northwestern Univ., Evanston, IL (United States). Dept. of Chemistry
  4. Northwestern Univ., Evanston, IL (United States). Dept. of Chemistry; King Abdulaziz Univ., Jeddah (Saudi Arabia)
Publication Date:
Grant/Contract Number:
SC0008688; FG02-08ER15967; FG02-12ER16362
Type:
Accepted Manuscript
Journal Name:
Science Advances
Additional Journal Information:
Journal Volume: 2; Journal Issue: 10; Journal ID: ISSN 2375-2548
Publisher:
AAAS
Research Org:
Univ. of Minnesota, Minneapolis (United States); Northwestern Univ., Evanston, IL (United States); Nanoporous Materials Genome Center
Sponsoring Org:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22); USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22). Chemical Sciences, Geosciences & Biosciences Division
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; Pre-combustion carbon capture; genetic algorithm; high-throughput material screening; molecular simulation; materials genome
OSTI Identifier:
1468797
Alternate Identifier(s):
OSTI ID: 1476385

Chung, Y. G., Gomez-Gualdron, D. A., Li, P., Leperi, K. T., Deria, P., Zhang, H., Vermeulen, N. A., Stoddart, J. F., You, F., Hupp, J. T., Farha, O. K., and Snurr, R. Q.. In silico discovery of metal-organic frameworks for precombustion CO2 capture using a genetic algorithm. United States: N. p., Web. doi:10.1126/sciadv.1600909.
Chung, Y. G., Gomez-Gualdron, D. A., Li, P., Leperi, K. T., Deria, P., Zhang, H., Vermeulen, N. A., Stoddart, J. F., You, F., Hupp, J. T., Farha, O. K., & Snurr, R. Q.. In silico discovery of metal-organic frameworks for precombustion CO2 capture using a genetic algorithm. United States. doi:10.1126/sciadv.1600909.
Chung, Y. G., Gomez-Gualdron, D. A., Li, P., Leperi, K. T., Deria, P., Zhang, H., Vermeulen, N. A., Stoddart, J. F., You, F., Hupp, J. T., Farha, O. K., and Snurr, R. Q.. 2016. "In silico discovery of metal-organic frameworks for precombustion CO2 capture using a genetic algorithm". United States. doi:10.1126/sciadv.1600909. https://www.osti.gov/servlets/purl/1468797.
@article{osti_1468797,
title = {In silico discovery of metal-organic frameworks for precombustion CO2 capture using a genetic algorithm},
author = {Chung, Y. G. and Gomez-Gualdron, D. A. and Li, P. and Leperi, K. T. and Deria, P. and Zhang, H. and Vermeulen, N. A. and Stoddart, J. F. and You, F. and Hupp, J. T. and Farha, O. K. and Snurr, R. Q.},
abstractNote = {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. Here in this paper 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.},
doi = {10.1126/sciadv.1600909},
journal = {Science Advances},
number = 10,
volume = 2,
place = {United States},
year = {2016},
month = {10}
}