DFT Study on the Complex Reaction Networks in the Conversion of Ethylene to Ethylidyne on Flat and Stepped Pd

2009 ◽  
Vol 113 (19) ◽  
pp. 8278-8286 ◽  
Author(s):  
J. Andersin ◽  
N. Lopez ◽  
K. Honkala
Keyword(s):  
Dft Study ◽  
Reaction Networks ◽  
Complex Reaction

scholarly journals Non-normality and non-monotonic dynamics in complex reaction networks

2020 ◽  
Vol 2 (4) ◽  
Author(s):  
Zachary G. Nicolaou ◽  
Takashi Nishikawa ◽  
Schuyler B. Nicholson ◽  
Jason R. Green ◽  
Adilson E. Motter
Keyword(s):  
Reaction Networks ◽  
Complex Reaction

scholarly journals Uncertainty quantification for quantum chemical models of complex reaction networks

2016 ◽  
Vol 195 ◽  
pp. 497-520 ◽  
Author(s):  
Jonny Proppe ◽  
Tamara Husch ◽  
Gregor N. Simm ◽  
Markus Reiher
Keyword(s):  
Free Energy ◽  
Electronic Structure ◽  
State Space ◽  
Time Scales ◽  
Reaction Network ◽  
Reaction Networks ◽  
Multiple Time Scales ◽  
Complex Reaction ◽  
Discrete State ◽  
Continuous State

For the quantitative understanding of complex chemical reaction mechanisms, it is, in general, necessary to accurately determine the corresponding free energy surface and to solve the resulting continuous-time reaction rate equations for a continuous state space. For a general (complex) reaction network, it is computationally hard to fulfill these two requirements. However, it is possible to approximately address these challenges in a physically consistent way. On the one hand, it may be sufficient to consider approximate free energies if a reliable uncertainty measure can be provided. On the other hand, a highly resolved time evolution may not be necessary to still determine quantitative fluxes in a reaction network if one is interested in specific time scales. In this paper, we present discrete-time kinetic simulations in discrete state space taking free energy uncertainties into account. The method builds upon thermo-chemical data obtained from electronic structure calculations in a condensed-phase model. Our kinetic approach supports the analysis of general reaction networks spanning multiple time scales, which is here demonstrated for the example of the formose reaction. An important application of our approach is the detection of regions in a reaction network which require further investigation, given the uncertainties introduced by both approximate electronic structure methods and kinetic models. Such cases can then be studied in greater detail with more sophisticated first-principles calculations and kinetic simulations.

scholarly journals More and Faster: Simultaneously Improving Reaction Coverage and Computational Cost in Automated Reaction Prediction Tasks

2020 ◽  
Author(s):  
Qiyuan Zhao ◽  
Brett Savoie
Keyword(s):  
Transition State ◽  
Domain Knowledge ◽  
Low Cost ◽  
Computational Cost ◽  
Chemical Degradation ◽  
Reaction Pathways ◽  
Reaction Networks ◽  
Complex Reaction ◽  
Success Rates ◽  
Reaction Prediction

<div> <div> <div> <p>Automated reaction prediction has the potential to elucidate complex reaction networks for applications ranging from combustion to materials degradation. Although substantial progress has been made in predicting specific reaction pathways and resolving mechanisms, the computational cost and inconsistent reaction coverage of automated prediction are still obstacles to exploring deep reaction networks without using heuristics. Here we show that cost can be reduced and reaction coverage can be increased simultaneously by relatively straight- forward modifications of the reaction enumeration, geometry initialization, and transition state convergence algorithms that are common to many emerging prediction methodologies. These changes are implemented in the context of Yet Another Reaction Program (YARP), our reaction prediction package, for which we report a head-to-head comparison with prevailing methods for two benchmark reaction prediction tasks. In all cases, we observe near perfect recapitulation of established reaction pathways and products by YARP, without the use of heuristics or other domain knowledge to guide reaction selection. In addition, YARP also discovers many new kinetically relevant pathways and products reported here for the first time. This is achieved while simultaneously reducing the cost of reaction characterization nearly 100-fold and increasing transition state success rates and intended rates over 2-fold and 10-fold, respectively, compared with recent benchmarks. This combination of ultra-low cost and high reaction-coverage creates opportunities to explore the reactivity of larger sys- tems and more complex reaction networks for applications like chemical degradation, where approaches based on domain heuristics fail. </p> </div> </div> </div>

Complex reaction networks in high temperature hydrocarbon chemistry

2015 ◽  
Vol 17 (12) ◽  
pp. 7972-7985 ◽  
Author(s):  
İbrahim Mutlay ◽  
Albeiro Restrepo
Keyword(s):  
High Temperature ◽  
Complex Network ◽  
Chemical Kinetics ◽  
Network Theory ◽  
Reaction Networks ◽  
Complex Reaction ◽  
Complex Network Theory ◽  
Kinetics Of ◽  
Hydrocarbon Chemistry

Complex network theory reveals novel insights into the chemical kinetics of high temperature hydrocarbon decomposition.

scholarly journals Markovian dynamics on complex reaction networks

2013 ◽  
Vol 529 (2) ◽  
pp. 199-264 ◽  
Author(s):  
J. Goutsias ◽  
G. Jenkinson
Keyword(s):  
Reaction Networks ◽  
Complex Reaction ◽  
Markovian Dynamics

scholarly journals Multiscale and Innovative Kinetic Approaches in Heterogeneous Catalysis

Catalysts ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 501
Author(s):  
Yves Schuurman ◽  
Pascal Granger
Keyword(s):  
Heterogeneous Catalysis ◽  
Catalytic Reactions ◽  
Reaction Networks ◽  
Complex Reaction ◽  
Reactor Modeling ◽  
Heterogeneous Catalytic

Kinetics and reactor modeling for heterogeneous catalytic reactions are prominent tools for investigating, and understanding, the catalyst functionalities at nanoscale, and related rates of complex reaction networks [...]

scholarly journals An analysis of complex reaction networks in groundwater modeling

1998 ◽  
Vol 34 (7) ◽  
pp. 1767-1780 ◽  
Author(s):  
Ashok Chilakapati ◽  
Timothy Ginn ◽  
James Szecsody
Keyword(s):  
Groundwater Modeling ◽  
Reaction Networks ◽  
Complex Reaction
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