First-principles–based reaction kinetics from reactive molecular dynamics simulations: Application to hydrogen peroxide decomposition
Abstract
This paper presents our vision of how to use in silico approaches to extract the reaction mechanisms and kinetic parameters for complex condensed-phase chemical processes that underlie important technologies ranging from combustion to chemical vapor deposition. The goal is to provide an analytic description of the detailed evolution of a complex chemical system from reactants through various intermediates to products, so that one could optimize the efficiency of the reactive processes to produce the desired products and avoid unwanted side products. We could start with quantum mechanics (QM) to ensure an accurate description; however, to obtain useful kinetics we need to average over ∼10-nm spatial scales for ∼1 ns, which is prohibitively impractical with QM. Instead, we use the reactive force field (ReaxFF) trained to fit QM to carry out the reactive molecular dynamics (RMD). We focus here on showing that it is practical to extract from such RMD the reaction mechanisms and kinetics information needed to describe the reactions analytically. This analytic description can then be used to incorporate the correct reaction chemistry from the QM/ReaxFF atomistic description into larger-scale simulations of ∼10 nm to micrometers to millimeters to meters using analytic approaches of computational fluid dynamics and/or continuum chemical dynamics. In the paper we lay out the strategy to extract the mechanisms and rate parameters automatically without the necessity of knowing any details of the chemistry. We consider this to be a proof of concept. We refer to the process as RMD2Kin (reactive molecular dynamics to kinetics) for the general approach and as ReaxMD2Kin (ReaxFF molecular dynamics to kinetics) for QM-ReaxFF–based reaction kinetics.
Additional Information
© 2018 National Academy of Sciences. Published under the PNAS license. Edited by Katepalli R. Sreenivasan, New York University, New York, NY, and approved August 14, 2018 (received for review December 20, 2017). We thank Dr. Robert J. Nielsen (Smith), Dr. Sergey Zybin, Prof. Snezhana I. Abarzhi, and Aadyot Bhatnagar for helpful discussions. This work was supported by the US Department of Energy, Office of Energy Efficiency and Renewable Energy, Advanced Manufacturing Office Next Generation R&D Projects under Contract DE-AC07-05ID14517 (program manager Dickson Ozokwelu, in collaboration with Idaho National Laboratories, Rebecca Fushimi), by Office of Naval Research (Contract N00014-12-1-0538, Cliff Bedford and Chad A. Stoltz, program managers), and by the California Institute of Technology Toni and Bob Perpall and Ernest H Swift Summer Undergraduate Research Fellowships. This work was supported by resources provided by the Pawsey Supercomputing Centre with funding from the Australian Government and the Government of Western Australia through collaboration with Prof. Snezhana I. Abarzhi at the University of Western Australia. This work used the Extreme Science and Engineering Discovery Environment, which is supported by National Science Foundation Grant ACI-1548562.Attached Files
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Supplemental Material - pnas.1701383115.sapp.pdf
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Additional details
- PMCID
- PMC6744889
- Eprint ID
- 89900
- DOI
- 10.1073/pnas.1701383115
- Resolver ID
- CaltechAUTHORS:20180925-083300654
- DE-AC07-05ID14517
- Department of Energy (DOE)
- N00014-12-1-0538
- Office of Naval Research (ONR)
- Caltech Summer Undergraduate Research Fellowship (SURF)
- Australian Government
- Government of Western Australia
- ACI-1548562
- NSF
- Created
-
2018-09-25Created from EPrint's datestamp field
- Updated
-
2021-11-16Created from EPrint's last_modified field
- Other Numbering System Name
- WAG
- Other Numbering System Identifier
- 1313