Automated Skeleton Network Generation for ReaxFF Molecular Dynamics Simulations of Hydrocarbon Fuel Pyrolysis and Oxidation via a Rate-Based Algorithm
In this study, we present an automated approach of rate-based skeleton network generation for ReaxFF MD simulation (RxMD-SN) for deriving the reaction kinetic mechanism of large hydrocarbon fuels in pyrolysis and oxidation from large-scale ReaxFF MD simulations. The approach contains the statistical...
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| Published in | Journal of chemical theory and computation Vol. 20; no. 13; pp. 5539 - 5557 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
American Chemical Society
09.07.2024
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1549-9618 1549-9626 1549-9626 |
| DOI | 10.1021/acs.jctc.4c00409 |
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| Abstract | In this study, we present an automated approach of rate-based skeleton network generation for ReaxFF MD simulation (RxMD-SN) for deriving the reaction kinetic mechanism of large hydrocarbon fuels in pyrolysis and oxidation from large-scale ReaxFF MD simulations. The approach contains the statistical calculation of reaction rate constants and the generation of skeleton reaction networks using a rate-based algorithm. The RxMD-SN method takes advantage of reaction flux ranking at a small time interval in terms of temporal reaction rate to extract the core reaction networks, which allows for keeping the rare reaction events that may be dominant in a certain period of the reaction network. The kinetic models derived from ReaxFF MD simulation in CH4 oxidation can reproduce what was obtained in the ReaxFF MD simulation, which demonstrates the capability of RxMD-SN in capturing the global reaction kinetics. An evaluation of reaction rate constants indicates that close kinetic parameters are shared for n-octane oxidation of similar reaction classes, shared oxidation reactions of CH4 against n-heptane, and shared pyrolysis reactions of the RP-3 surrogate fuel against n-heptane. This capability of RxMD-SN is particularly beneficial in meeting the challenges in characterizing the oxidation reaction kinetics of large hydrocarbon molecules. RxMD-SN approach is potentially a general approach in chemical kinetics modeling on the basis of ReaxFF MD simulations. |
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| AbstractList | In this study, we present an automated approach of rate-based skeleton network generation for ReaxFF MD simulation (RxMD-SN) for deriving the reaction kinetic mechanism of large hydrocarbon fuels in pyrolysis and oxidation from large-scale ReaxFF MD simulations. The approach contains the statistical calculation of reaction rate constants and the generation of skeleton reaction networks using a rate-based algorithm. The RxMD-SN method takes advantage of reaction flux ranking at a small time interval in terms of temporal reaction rate to extract the core reaction networks, which allows for keeping the rare reaction events that may be dominant in a certain period of the reaction network. The kinetic models derived from ReaxFF MD simulation in CH4 oxidation can reproduce what was obtained in the ReaxFF MD simulation, which demonstrates the capability of RxMD-SN in capturing the global reaction kinetics. An evaluation of reaction rate constants indicates that close kinetic parameters are shared for n-octane oxidation of similar reaction classes, shared oxidation reactions of CH4 against n-heptane, and shared pyrolysis reactions of the RP-3 surrogate fuel against n-heptane. This capability of RxMD-SN is particularly beneficial in meeting the challenges in characterizing the oxidation reaction kinetics of large hydrocarbon molecules. RxMD-SN approach is potentially a general approach in chemical kinetics modeling on the basis of ReaxFF MD simulations. In this study, we present an automated approach of rate-based skeleton network generation for ReaxFF MD simulation (RxMD-SN) for deriving the reaction kinetic mechanism of large hydrocarbon fuels in pyrolysis and oxidation from large-scale ReaxFF MD simulations. The approach contains the statistical calculation of reaction rate constants and the generation of skeleton reaction networks using a rate-based algorithm. The RxMD-SN method takes advantage of reaction flux ranking at a small time interval in terms of temporal reaction rate to extract the core reaction networks, which allows for keeping the rare reaction events that may be dominant in a certain period of the reaction network. The kinetic models derived from ReaxFF MD simulation in CH4 oxidation can reproduce what was obtained in the ReaxFF MD simulation, which demonstrates the capability of RxMD-SN in capturing the global reaction kinetics. An evaluation of reaction rate constants indicates that close kinetic parameters are shared for n-octane oxidation of similar reaction classes, shared oxidation reactions of CH4 against n-heptane, and shared pyrolysis reactions of the RP-3 surrogate fuel against n-heptane. This capability of RxMD-SN is particularly beneficial in meeting the challenges in characterizing the oxidation reaction kinetics of large hydrocarbon molecules. RxMD-SN approach is potentially a general approach in chemical kinetics modeling on the basis of ReaxFF MD simulations.In this study, we present an automated approach of rate-based skeleton network generation for ReaxFF MD simulation (RxMD-SN) for deriving the reaction kinetic mechanism of large hydrocarbon fuels in pyrolysis and oxidation from large-scale ReaxFF MD simulations. The approach contains the statistical calculation of reaction rate constants and the generation of skeleton reaction networks using a rate-based algorithm. The RxMD-SN method takes advantage of reaction flux ranking at a small time interval in terms of temporal reaction rate to extract the core reaction networks, which allows for keeping the rare reaction events that may be dominant in a certain period of the reaction network. The kinetic models derived from ReaxFF MD simulation in CH4 oxidation can reproduce what was obtained in the ReaxFF MD simulation, which demonstrates the capability of RxMD-SN in capturing the global reaction kinetics. An evaluation of reaction rate constants indicates that close kinetic parameters are shared for n-octane oxidation of similar reaction classes, shared oxidation reactions of CH4 against n-heptane, and shared pyrolysis reactions of the RP-3 surrogate fuel against n-heptane. This capability of RxMD-SN is particularly beneficial in meeting the challenges in characterizing the oxidation reaction kinetics of large hydrocarbon molecules. RxMD-SN approach is potentially a general approach in chemical kinetics modeling on the basis of ReaxFF MD simulations. In this study, we present an automated approach of rate-based skeleton network generation for ReaxFF MD simulation (RxMD-SN) for deriving the reaction kinetic mechanism of large hydrocarbon fuels in pyrolysis and oxidation from large-scale ReaxFF MD simulations. The approach contains the statistical calculation of reaction rate constants and the generation of skeleton reaction networks using a rate-based algorithm. The RxMD-SN method takes advantage of reaction flux ranking at a small time interval in terms of temporal reaction rate to extract the core reaction networks, which allows for keeping the rare reaction events that may be dominant in a certain period of the reaction network. The kinetic models derived from ReaxFF MD simulation in CH oxidation can reproduce what was obtained in the ReaxFF MD simulation, which demonstrates the capability of RxMD-SN in capturing the global reaction kinetics. An evaluation of reaction rate constants indicates that close kinetic parameters are shared for -octane oxidation of similar reaction classes, shared oxidation reactions of CH against -heptane, and shared pyrolysis reactions of the RP-3 surrogate fuel against -heptane. This capability of RxMD-SN is particularly beneficial in meeting the challenges in characterizing the oxidation reaction kinetics of large hydrocarbon molecules. RxMD-SN approach is potentially a general approach in chemical kinetics modeling on the basis of ReaxFF MD simulations. |
| Author | Li, Xiaoxia Ren, Chunxing Xiao, Yuanyuan Zheng, Mo |
| AuthorAffiliation | State Key Laboratory of Mesoscience and Engineering Innovation Academy for Green Manufacture |
| AuthorAffiliation_xml | – name: State Key Laboratory of Mesoscience and Engineering – name: Innovation Academy for Green Manufacture |
| Author_xml | – sequence: 1 givenname: Yuanyuan surname: Xiao fullname: Xiao, Yuanyuan organization: Innovation Academy for Green Manufacture – sequence: 2 givenname: Mo orcidid: 0000-0002-4467-5494 surname: Zheng fullname: Zheng, Mo email: mzheng@ipe.ac.cn organization: Innovation Academy for Green Manufacture – sequence: 3 givenname: Xiaoxia orcidid: 0000-0002-1178-124X surname: Li fullname: Li, Xiaoxia email: xxia@ipe.ac.cn organization: Innovation Academy for Green Manufacture – sequence: 4 givenname: Chunxing orcidid: 0000-0003-4402-4859 surname: Ren fullname: Ren, Chunxing organization: Innovation Academy for Green Manufacture |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38937883$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | Algorithms Automation Heptanes Hydrocarbon fuels Hydrocarbons Kinetics Methane Molecular dynamics Octane Oxidation Pyrolysis Rate constants Reaction kinetics Reaction Mechanisms Scale (corrosion) Simulation |
| Title | Automated Skeleton Network Generation for ReaxFF Molecular Dynamics Simulations of Hydrocarbon Fuel Pyrolysis and Oxidation via a Rate-Based Algorithm |
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