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@PHDTHESIS{Schmalz:1025143,
author = {Schmalz, Felix},
othercontributors = {Leonhard, Kai and Heufer, Karl Alexander and Goudeli,
Eirini},
title = {{I}mproved combustion and pyrolysis reaction network
exploration with reactive molecular dynamics},
school = {Rheinisch-Westfälische Technische Hochschule Aachen},
type = {Dissertation},
address = {Aachen},
publisher = {RWTH Aachen University},
reportid = {RWTH-2026-00553},
pages = {1 Online-Ressource : Illustrationen},
year = {2025},
note = {Cotutelle-Dissertation. - Veröffentlicht auf dem
Publikationsserver der RWTH Aachen University 2026;
Dissertation, Rheinisch-Westfälische Technische Hochschule
Aachen, 2025. - Dissertation, University of Melbourne, 2024},
abstract = {Chemical mechanisms are essential for modeling chemical
processes, particularly in combustion and pyrolysis, where
high temperatures produce a wide range of species. The
complexity of these processes, compounded by the
introduction of biomass-derived fuels, presents challenges
in applying existing knowledge about reaction rates and
pathways from fossil fuel combustion. Reactive Molecular
Dynamics (RMD) simulations offer a way to explore reactions
with minimal prior knowledge, making this approach ideal for
combustion and pyrolysis studies. ChemTraYzer, a tool that
automates RMD analysis, identifies the reactions occurring
during simulations. However, the complexity of these
processes often results in extensive reaction networks,
complicating the understanding of the underlying mechanisms.
In this thesis, I evaluate two reaction exploration methods
using ChemTraYzer and accelerated dynamics to identify
reactions of ethyl-2-yl formate, an intermediate in biofuel
combustion. Both methods successfully identify key pathways,
including decomposition, cyclization, and hydrogen
migration. By comparing the results, I find that each method
discovers additional reaction pathways that complement the
other. This suggests that using both methods together
provides broader coverage of the reaction space while
keeping computational costs manageable. I also tackle the
challenge of analyzing the complex reaction networks
generated by RMD simulations. I develop a methodology that
integrates ChemTraYzer with the Nudged-Elastic-Band (NEB)
method to identify and validate key reaction paths,
extending existing chemical mechanisms. Although ReaxFF is
used for rapid force calculations, I show that validating
these reactions with higher-level quantum mechanical methods
is essential due to ReaxFF’s limitations, particularly in
computing reaction barriers and addressing spin
conservation. Hydrocarbon pyrolysis and soot formation serve
as the case study, generating a large and complex reaction
network. Finally, I propose a method for classifying
reactions based on data obtained from RMD simulations. This
classification approach simplifies large reaction networks
by abstracting reaction pathways into broader categories,
facilitating a clearer understanding of complex chemical
processes.},
cin = {412110},
ddc = {620},
cid = {$I:(DE-82)412110_20140620$},
pnm = {AutoCheMo - Automatic generation of Chemical Models
(814143) / DFG project G:(GEPRIS)322657802 - Entwicklung
einer Methodik zur Bestimmung von Reaktionspfaden und -raten
komplexer Verbrennungsreaktionsnetzwerke (322657802) / DFG
project G:(GEPRIS)390919832 - EXC 2186: Das Fuel Science
Center – Adaptive Umwandlungssysteme für erneuerbare
Energie- und Kohlenstoffquellen (390919832)},
pid = {G:(EU-Grant)814143 / G:(GEPRIS)322657802 /
G:(GEPRIS)390919832},
typ = {PUB:(DE-HGF)11},
doi = {10.18154/RWTH-2026-00553},
url = {https://publications.rwth-aachen.de/record/1025143},
}
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