Implementation of Genetic Algorithms to Optimize Metal–Organic Frameworks for CO 2 Capture
Metal-organic frameworks (MOFs) are promising materials for CO capture with the potential to use less energy than current industrial CO capture methods. MOFs are highly versatile sorbents, and there is an almost unlimited number of MOFs that could be synthesized. In this work, we used a genetic algo...
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| Published in | Langmuir Vol. 41; no. 7; pp. 4585 - 4593 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
United States
25.02.2025
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| Online Access | Get full text |
| ISSN | 0743-7463 1520-5827 |
| DOI | 10.1021/acs.langmuir.4c04386 |
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| Abstract | Metal-organic frameworks (MOFs) are promising materials for CO
capture with the potential to use less energy than current industrial CO
capture methods. MOFs are highly versatile sorbents, and there is an almost unlimited number of MOFs that could be synthesized. In this work, we used a genetic algorithm (GA) and grand canonical Monte Carlo (GCMC) simulations to efficiently search for high-performing MOFs for CO
capture. We analyzed the effects of important GA parameters, including the mutation probability, the number of MOFs per generation, and the number of GA generations, on the GA performance. We performed GCMC simulations on-the-fly during the GA procedure to determine the performance of proposed MOFs and optimized their structures using multiple objective functions across different topologies. The GA was able to determine top-performing MOFs balancing CO
selectivity versus working capacity and reduced the cost of molecular simulations by a factor of 25 versus brute-force screening of an entire database of structures. |
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| AbstractList | Metal-organic frameworks (MOFs) are promising materials for CO
capture with the potential to use less energy than current industrial CO
capture methods. MOFs are highly versatile sorbents, and there is an almost unlimited number of MOFs that could be synthesized. In this work, we used a genetic algorithm (GA) and grand canonical Monte Carlo (GCMC) simulations to efficiently search for high-performing MOFs for CO
capture. We analyzed the effects of important GA parameters, including the mutation probability, the number of MOFs per generation, and the number of GA generations, on the GA performance. We performed GCMC simulations on-the-fly during the GA procedure to determine the performance of proposed MOFs and optimized their structures using multiple objective functions across different topologies. The GA was able to determine top-performing MOFs balancing CO
selectivity versus working capacity and reduced the cost of molecular simulations by a factor of 25 versus brute-force screening of an entire database of structures. |
| Author | Pham, Thang D. Snurr, Randall Q. |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39950599$$D View this record in MEDLINE/PubMed |
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