In silico discovery of metal-organic frameworks for precombustion CO 2 capture using a genetic algorithm
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A genetic algorithm was used to accelerate the computational discovery of new nanoporous materials for capturing CO 2 .
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Optimization of a single property is feasible with either direct experimentation or physics-based modeling but can often be accelerated with machine learning.
“Optimization of a single property is feasible with either direct experimentation (117, 118) or physics-based modeling (119) but can often be accelerated with ML (43, 120, 121 ).”
From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [119]
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- Authors
- Chung, Y. G., Gómez-Gualdrón, 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.
- Affiliations
- Northwestern University (US)
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, USA.
- Corresponding author
- Chung, Yongchul G.
- Snurr, Randall Q.
- ORCID
- Venue
- Science Advances
- Published
- 2016-10-07
- Volume
- 2
- Issue
- 10
- Pages
- e1600909-e1600909
- ISSN
- 2375-2548 (ISSN-L)
- PMC
- PMC5065252
- OpenAlex
- W2530805533
- Cited by
- 321
- References
- 64
- Citations by year
- 2026: 26 · 2025: 34 · 2024: 33 · 2023: 35 · 2022: 45 · 2021: 48 · 2020: 35 · 2019: 32 · 2018: 22 · 2017: 9 · 2016: 2
- FWCI
- 8.54
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by-nc
- Locations
- Science Advances (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- U.S. Department of Energy (ID0EI2CI, DE-FG02-12ER16362, ID0EG6CI, DE-FG02-08ER15967, ID0EIDDI)
- King Abdulaziz City for Science and Technology (ID0EEHDI)
- Northwestern University (ID0E6LDI)
- Stanford University (ID0E1QDI, Global Climate and Energy Project)
- Topics
- Physical Sciences › Chemistry › Inorganic Chemistry › Metal-Organic Frameworks: Synthesis and Applications
- Physical Sciences › Engineering › Mechanical Engineering › Carbon Dioxide Capture Technologies
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Keywords
- metal-organic frameworks, genetic algorithm, CO2 adsorption capacity, CO2 capture
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