First-principles monte carlo simulations of reaction equilibria in compressed vapors

Evgenii O. Fetisov, I. Feng William Kuo, Chris Knight, Joost Vande Vondele, Troy Van Voorhis, J. Ilja Siepmann

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

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 NO2 and N2O in mole fractions approaching 1%, whereas N3 and O3 are not observed. The equilibrium distributions obtained from the RxFPMC simulations agree well with those from a thermochemical computer code parametrized to experimental data.

Original languageEnglish (US)
Pages (from-to)409-415
Number of pages7
JournalACS Central Science
Volume2
Issue number6
DOIs
StatePublished - Jun 22 2016

Bibliographical note

Funding Information:
This work was supported by the National Science Foundation through Grant CHE-1265849. Part of this work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DEAC52- 07NA27344. This research used resources of the Argonne Leadership Computing Facility, which is aDOEOffice of Science User Facility supported under Contract DE-AC02-06CH11357. Additional computer resources were provided by the Minnesota Supercomputing Institute at the University of Minnesota.

Publisher Copyright:
© 2016 American Chemical Society.

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