ChemTraYzer - Reaction Models from Molecular Dynamics Simulations

 

The accurate description of thermodynamic molecular properties and reaction kinetics is a crucial component of designing and optimizing new and existing combustion engine concepts. However, the development of reaction models that represent the combustion mechanisms of a fuel is often a time-consuming process, as hundreds of species and reactions need to be identified and quantified. Reaction kinetics and thermodynamic molecular properties can be determined using experiments or ab-initio methods. Both approaches are associated with a significant amount of time and effort due to the multitude of species and reactions that need to be quantified.

 

Reactive molecular dynamics (rMD) simulations allow for the efficient and parallel investigation of complex mechanisms, including a variety of species. Typical applications of rMD include not only combustion and ignition processes but also polymerization, catalysis, or bioprocesses. Calculating energies and forces using the force fields underlying rMD simulations is orders of magnitude faster than ab-initio methods, but the resulting kinetics and thermodynamic data are less precise.

  Copyright: © Lehrstuhl fuer Technische Thermodynamik der RWTH Aachen

In our software package 'Chemical Trajectory Analyzer' (ChemTraYzer) [1,2,3], rMD simulations are used to find reaction pathways and networks. By automatically combining them with quantum mechanical optimizations for species and transition states (QM), the accuracy of kinetics and thermodynamic predictions is enhanced. Based on the molecule geometries optimized at the quantum mechanical level, thermodynamic data and reaction rate constants are generated using the Eyring theory. A schematic program structure is shown in the figure. The software package is available as open-source software under the MIT license. ChemTraYzer is also a part of the commercial software Amsterdam Modeling Suite by the Amsterdam-based company Software for Chemistry & Materials (SCM).

Currently, we are continuing the development of ChemTraYzer in three projects:

• In a project funded by the DFG (German Research Foundation) (LE 2221/15-1), we use ChemTraYzer to address challenges in mechanism development that could not be solved using traditional approaches. Specifically, we investigate the reaction pathways and rate coefficients of acetaldehyde oxidation and the formation of polycyclic aromatic hydrocarbons (PAHs) during the combustion and pyrolysis of n-heptane. We have already expanded the ChemTraYzer approach to automatically reoptimized quantum mechanical rate constants of the transition state theory (TST) (see figure) [2]. In a recent publication, we have shown a computationally efficient way to improve the accuracy of rate constants by combining non-TST information from MD trajectories and quantum mechanical TST rate constants [6]. Since some important ignition processes, such as low-temperature ignition, are too slow to be uncovered with conventional MD, we have developed acceleration methods called 'Pressure Accelerated Dynamics' [7] and 'ChemTraYzer Temperature Accelerated Dynamics' [8] to extend the possible simulation times.
• In collaboration with the Laboratory of Aerosol Particle Technology at the University of Melbourne, we investigate the process of initial soot formation. Using ReaxFF in MD simulations, we study the nucleation of soot precursors such as PAHs in the gas phase and particle growth through gas-surface reactions. ChemTraYzer enables the creation of a reaction network and assists in identifying reaction types that are important for soot formation. This facilitates the interpretation of soot-forming chemistry and the use of these reaction types in larger-scale models.
• In the Fuel Science Center (FSC Grant No. EXC 2186/1) Excellence Cluster, we study the production and combustion of unconventional biohybrid fuels on scales ranging from the atomic to the device level. We employ ChemTraYzer to discover novel reaction pathways that are necessary to understand the ignition behavior and soot formation of novel fuels and their blends, allowing us to develop fuels with long-term improved properties. Here, ChemTraYzer complements existing mechanisms by suggesting new, unconventional pathways.

Relevant publications:

[1] Döntgen et al., J. Chem. Theory Comput. 11 (2015), 2517-2524
[2] Döntgen et al., J. Chem. Inf. Model. 58 (2018), 1343-1355
[3] Kröger et al., JCTC 13 (2017), 3955-3960
[4] van Duin et al., J. Phys. Chem. A 105 (2001), 9396-9409
[5] Chenoweth et al. J. Phys. Chem. A 112 (2008), 1040-1053
[6] Schmalz et al. ACS Omega. 5(5), (2020), 2242–2253
[7] Krep et al. Chem. Systems Chem. 2 (2020), e1900043
[8] Krep et al., J. Chem. Inf. Model. 62 (2022), 890-902
[9] Krep et al., ChemPhysChem. 24 (2023), e202200786