KinBot: Automated stationary point search on potential energy surfaces

Vijver, Ruben Van de; Zádor, Judit

doi:10.1016/j.cpc.2019.106947

Cited by 111 publications

(130 citation statements)

References 47 publications

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“…The PES for initial decomposition of 3‐carene was explored using KinBot, 18 which is designed to automatically crawl on PESs to find all kinetically relevant stationary points. It starts from 3‐carene to search for connected transition states, from which it identifies product species with Intrinsic Reaction Coordinate calculations.…”

Section: Methodsmentioning

confidence: 99%

“…Comparing with the existing model of Sharath et al., 17 which only considers small species, a more detailed and extensive model has been generated that also considers the formation of aromatics. The potential energy surface (PES) for the initial decomposition pathways is studied with the help of KinBot, which is an automatic PES exploration tool 18 . A kinetic model for 3‐carene initial decomposition was generated with the help of the in‐house automatic kinetic model generation tool Genesys 19,20 …”

Section: Introductionmentioning

confidence: 99%

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Detailed experimental and kinetic modeling study of 3‐carene pyrolysis

Zhang

Vermeire

Vijver

et al. 2020

Int J of Chemical Kinetics

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3-Carene is an important potential biofuel with properties similar to the jetpropellant JP-10. Its thermal decomposition and combustion behavior is to date unknown, which is essential to assess its quality as a fuel. A combined experimental and kinetic modeling study has been conducted to understand the initial decomposition of 3-carene. The pyrolysis of 3-carene was investigated in a jet-stirred quartz reactor at atmospheric pressure, at temperatures varying from 650 to 1050 K, covering the complete conversion range. The decomposition of 3carene was observed to start around 800 K, and it is almost complete at 970 K. Online gas chromatography shows that primarily aromatics are generated which suggests that 3-carene is not a good fuel candidate. The potential energy surface for the initial decomposition pathways determined by KinBot shows that a hydrogen elimination reaction dominates, giving primarily cara-2,4-diene. Next to this molecular pathway, radical pathways lead to aromatics via ring opening. The kinetic model was automatically generated with Genesys and consists of 2565 species and 9331 reactions. New quantum chemical calculations at the CBS-QB3 level of theory were needed to calculate rate coefficients and thermodynamic properties relevant for the primary decomposition of 3-carene. Both the conversion of 3-carene and the yields of the primary products (ie, benzene and hydrogen gas) are well predicted with this kinetic model. Rate of production analyses shows that the dominant pathways to convert 3-carene are hydrogen elimination reaction and radical chemistry.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detailed experimental and kinetic modeling study of 3‐carene pyrolysis

Zhang

Vermeire

Vijver

et al. 2020

Int J of Chemical Kinetics

Self Cite

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show abstract

“…Often the people building the kinetic models are different from the people performing the quantum chemistry calculations, so the sequencing of the work has often been inefficient, driven primarily by practical considerations such as personnel availability and funding cycles. However, the increasing automation of steps in the workflow have partially ameliorated this problem: there are now several well‐developed software packages for constructing reaction mechanisms (e.g., 11‐19,108 ), and there has also been continuous progress in automating the quantum chemistry calculations, 27‐35 making it increasingly practical for a single person to both build the kinetic model and refine its parameters. For example, in one recent study all the thermochemical parameters of all the molecules in a large kinetic model were automatically computed using high level quantum chemistry 28 .…”

Section: Technical Issuesmentioning

confidence: 99%

“…For example, in one recent study all the thermochemical parameters of all the molecules in a large kinetic model were automatically computed using high level quantum chemistry 28 . Several groups are developing the capability to do automatic calculations of the rate coefficients as well 29‐33 . There have also been significant advances in methods for computing solvation effects 36,37 .…”

Section: Technical Issuesmentioning

confidence: 99%

Moving from postdictive to predictive kinetics in reaction engineering

Green

2020

AIChE Journal

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“…Thus, several heuristics have been developed to identify transition states, either based on reaction templates, artificial forces, or using the local curvature of the potential energy surface. 36,37,46,47,50,[61][62][63][64][65] Separately, several double-ended searching algorithms have been developed for situations where the start and end points (i.e., reactant and product states, respectively) are known in advance.…”

Section: Graph-based Product Enumerationmentioning

confidence: 99%

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

Zhao¹,

Savoie

2020

Preprint

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<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>

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KinBot: Automated stationary point search on potential energy surfaces

Cited by 111 publications

References 47 publications

Detailed experimental and kinetic modeling study of 3‐carene pyrolysis

Detailed experimental and kinetic modeling study of 3‐carene pyrolysis

Moving from postdictive to predictive kinetics in reaction engineering

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

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