First-Principles Monte Carlo Simulations of Reaction Equilibria in Compressed Vapors
Predictive modeling of reaction equilibria presents one of the grand challenges in the field of molecular simulation. Difficulties in the study of such systems arise from the need (i) to accurately model both strong, short-ranged interactions leading to the formation of chemical bonds and weak inter...
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| Format: | Article |
| Language: | en_US |
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American Chemical Society (ACS)
2017
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| Online Access: | http://hdl.handle.net/1721.1/109541 https://orcid.org/0000-0001-7111-0176 |
| _version_ | 1811094322723946496 |
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| author | Fetisov, Evgenii O. Kuo, I-Feng William Knight, Chris VandeVondele, Joost Van Voorhis, Troy Siepmann, J. Ilja |
| author2 | Massachusetts Institute of Technology. Department of Chemistry |
| author_facet | Massachusetts Institute of Technology. Department of Chemistry Fetisov, Evgenii O. Kuo, I-Feng William Knight, Chris VandeVondele, Joost Van Voorhis, Troy Siepmann, J. Ilja |
| author_sort | Fetisov, Evgenii O. |
| collection | MIT |
| description | Predictive modeling of reaction equilibria presents one of the grand challenges in the field of molecular simulation. Difficulties in the study of such systems arise from the need (i) to accurately model both strong, short-ranged interactions leading to the formation of chemical bonds and weak interactions arising from the environment, and (ii) to sample the range of time scales involving frequent molecular collisions, slow diffusion, and infrequent reactive events. Here we present a novel reactive first-principles Monte Carlo (RxFPMC) approach that allows for investigation of reaction equilibria without the need to prespecify a set of chemical reactions and their ideal-gas equilibrium constants. We apply RxFPMC to investigate a nitrogen/oxygen mixture at T = 3000 K and p = 30 GPa, i.e., conditions that are present in atmospheric lightning strikes and explosions. The RxFPMC simulations show that the solvation environment leads to a significantly enhanced NO concentration that reaches a maximum when oxygen is present in slight excess. In addition, the RxFPMC simulations indicate the formation of NO[subscript 2] and N[subscript 2]O in mole fractions approaching 1%, whereas N[subscript 3] and O[subscript 3] are not observed. The equilibrium distributions obtained from the RxFPMC simulations agree well with those from a thermochemical computer code parametrized to experimental data. |
| first_indexed | 2024-09-23T15:58:12Z |
| format | Article |
| id | mit-1721.1/109541 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T15:58:12Z |
| publishDate | 2017 |
| publisher | American Chemical Society (ACS) |
| record_format | dspace |
| spelling | mit-1721.1/1095412022-10-02T05:26:49Z First-Principles Monte Carlo Simulations of Reaction Equilibria in Compressed Vapors Fetisov, Evgenii O. Kuo, I-Feng William Knight, Chris VandeVondele, Joost Van Voorhis, Troy Siepmann, J. Ilja Massachusetts Institute of Technology. Department of Chemistry Van Voorhis, Troy Predictive modeling of reaction equilibria presents one of the grand challenges in the field of molecular simulation. Difficulties in the study of such systems arise from the need (i) to accurately model both strong, short-ranged interactions leading to the formation of chemical bonds and weak interactions arising from the environment, and (ii) to sample the range of time scales involving frequent molecular collisions, slow diffusion, and infrequent reactive events. Here we present a novel reactive first-principles Monte Carlo (RxFPMC) approach that allows for investigation of reaction equilibria without the need to prespecify a set of chemical reactions and their ideal-gas equilibrium constants. We apply RxFPMC to investigate a nitrogen/oxygen mixture at T = 3000 K and p = 30 GPa, i.e., conditions that are present in atmospheric lightning strikes and explosions. The RxFPMC simulations show that the solvation environment leads to a significantly enhanced NO concentration that reaches a maximum when oxygen is present in slight excess. In addition, the RxFPMC simulations indicate the formation of NO[subscript 2] and N[subscript 2]O in mole fractions approaching 1%, whereas N[subscript 3] and O[subscript 3] are not observed. The equilibrium distributions obtained from the RxFPMC simulations agree well with those from a thermochemical computer code parametrized to experimental data. National Science Foundation (U.S.) (Grant CHE-1265849) 2017-06-02T15:23:16Z 2017-06-02T15:23:16Z 2016-06 2016-04 Article http://purl.org/eprint/type/JournalArticle 2374-7943 2374-7951 http://hdl.handle.net/1721.1/109541 .Fetisov, Evgenii O. et al. “First-Principles Monte Carlo Simulations of Reaction Equilibria in Compressed Vapors.” ACS Central Science 2.6 (2016): 409–415. © 2016 American Chemical Society https://orcid.org/0000-0001-7111-0176 en_US http://dx.doi.org/10.1021/acscentsci.6b00095 ACS Central Science Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf American Chemical Society (ACS) ACS |
| spellingShingle | Fetisov, Evgenii O. Kuo, I-Feng William Knight, Chris VandeVondele, Joost Van Voorhis, Troy Siepmann, J. Ilja First-Principles Monte Carlo Simulations of Reaction Equilibria in Compressed Vapors |
| title | First-Principles Monte Carlo Simulations of Reaction Equilibria in Compressed Vapors |
| title_full | First-Principles Monte Carlo Simulations of Reaction Equilibria in Compressed Vapors |
| title_fullStr | First-Principles Monte Carlo Simulations of Reaction Equilibria in Compressed Vapors |
| title_full_unstemmed | First-Principles Monte Carlo Simulations of Reaction Equilibria in Compressed Vapors |
| title_short | First-Principles Monte Carlo Simulations of Reaction Equilibria in Compressed Vapors |
| title_sort | first principles monte carlo simulations of reaction equilibria in compressed vapors |
| url | http://hdl.handle.net/1721.1/109541 https://orcid.org/0000-0001-7111-0176 |
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