Spiers Memorial Lecture: Theory of unimolecular reactions

Klippenstein, Stephen J.

doi:10.1039/d2fd00125j

Cited by 10 publications

(13 citation statements)

References 219 publications

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“…This value may seem very low given that the model calculations by Jasper and Miller predicted 117 cm –1 for the smaller CH 3 • + H • (+ He) ⇌ CH 4 (+ He) system . However, one-dimensional ME treatments are known to overestimate rate coefficients in the falloff region due to the neglect of angular momentum effects . One can compensate for this by using an artificially low ⟨Δ E ⟩ down .…”

Section: Resultsmentioning

confidence: 99%

“…36 However, one-dimensional ME treatments are known to overestimate rate coefficients in the falloff region due to the neglect of angular momentum effects. 37 One can compensate for this by using an artificially low ⟨Δ E ⟩ down . Thus, the value we obtain for ⟨Δ E ⟩ down,300K may simply be low because we forced a one-dimensional model onto two-dimensional data.…”

Section: Resultsmentioning

confidence: 99%

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Temperature and Pressure Dependence of the Reaction between Ethyl Radical and Molecular Oxygen: Experiments and Master Equation Simulations

et al. 2023

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We have used laser-photolysis – photoionization mass-spectrometry to measure the rate coefficient for the reaction between ethyl radical and molecular oxygen as a function of temperature (190–801 K) and pressure (0.2–6 Torr) under pseudo-first-order conditions ([He] ≫ [O2] ≫ [C2H5 •]). Multiple ethyl precursor, photolysis wavelength, reactor material, and coating combinations were used. We reinvestigated the temperature dependence of the title reaction’s rate coefficient to resolve inconsistencies in existing data. The current results indicate that some literature values for the rate coefficient may indeed be slightly too large. The experimental work was complemented with master equation simulations. We used the current and some previous rate coefficient measurements to optimize the values of key parameters in the master equation model. After optimization, the model was able to reproduce experimental falloff curves and C2H4 + HO2 • yields. We then used the model to perform simulations over wide temperature (200–1500 K) and pressure (10–4–102 bar) ranges and provide the results in PLOG format to facilitate their use in atmospheric and combustion models.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Temperature and Pressure Dependence of the Reaction between Ethyl Radical and Molecular Oxygen: Experiments and Master Equation Simulations

et al. 2023

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“…An ideal automated kinetics framework should be able to carry out the steps of rate coefficient calculations much the same way an expert would do, just faster, on a larger scale, and without mistakes. The recent works of Klippenstein, , Green, and Miller et al provide an in-depth description of the cutting edge for theoretical kinetics calculations for gas-phase systems. In the following we highlight some aspects of these calculations that are hard to automate and briefly explain what the challenges are.…”

Section: Challenges Of Automated Reaction Kineticsmentioning

confidence: 99%

Automated Reaction Kinetics of Gas-Phase Organic Species over Multiwell Potential Energy Surfaces

et al. 2023

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Automation of rate-coefficient calculations for gas-phase organic species became possible in recent years and has transformed how we explore these complicated systems computationally. Kinetics workflow tools bring rigor and speed and eliminate a large fraction of manual labor and related error sources. In this paper we give an overview of this quickly evolving field and illustrate, through five detailed examples, the capabilities of our own automated tool, KinBot. We bring examples from combustion and atmospheric chemistry of C-, H-, O-, and N-atom-containing species that are relevant to molecular weight growth and autoxidation processes. The examples shed light on the capabilities of automation and also highlight particular challenges associated with the various chemical systems that need to be addressed in future work.

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“…Combustion chemistry in the gas phase is exceedingly complex, involving a large number of reactions among a rich variety of species. Discovering the myriad reaction pathways with their relevant participating species, and estimating their reaction barriers and rate coefficients with acceptable accuracy (⪅1 kcal/mol and within a factor of two, respectively), is a monumental task that has been a subject of research over decades [1][2][3][4]. Computational chemical kinetics studies necessary in this regard face a number of difficult challenges, including the combinatorial explosion of the number of possible reactions with the increasing number of relevant reactive species, the complexity and high-dimensionality of the potential energy surfaces (PESs) that define the reactive landscapes of large hydrocarbons, and the computational expense of accurate quantum chemical computations for large molecules.…”

Section: Introductionmentioning

confidence: 99%

Multifidelity neural network formulations for prediction of reactive molecular potential energy surfaces

Yang

Eldred

Zádor

et al. 2023

Preprint

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This paper focuses on the development of multifidelity modeling approaches using neural network surrogates, where training data arising from multiple model forms and resolutions are integrated to predict high-fidelity response quantities of interest at lower cost. We focus on the context of quantum chemistry and the integration of information from multiple levels of theory. Important foundations include the use of symmetry function-based atomic energy vector constructions as feature vectors for representing structures across families of molecules, and single-fidelity neural network training capabilities that learn the relationships needed to map feature vectors to potential energy predictions. These foundations are embedded within several multifidelity topologies that decompose the high-fidelity mapping into model-based components, including sequential formulations that admit a general nonlinear mapping across fidelities and discrepancy-based formulations that presume an additive decomposition. Methodologies are first explored and demonstrated on a pair of simple analytical test problems, and then deployed for potential energy prediction for C5H5 using B2PLYPD3 6-311++G(d,p) for high fidelity simulation data and Hartree Fock 6-31G for low fidelity data. For the common case of limited access to high-fidelity data, our computational results demonstrate that multifidelity neural network potential energy surface constructions achieve roughly an order of magnitude improvement, either in terms of test error reduction for equivalent total simulation cost or reduction in total cost for equivalent error.

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Spiers Memorial Lecture: Theory of unimolecular reactions

Abstract: One hundred years ago, at an earlier Faraday Discussion meeting, Lindemann presented a mechanism that provides the foundation for contemplating the pressure dependence of unimolecular reactions. Since that time, our...

Cited by 10 publications

References 219 publications

Temperature and Pressure Dependence of the Reaction between Ethyl Radical and Molecular Oxygen: Experiments and Master Equation Simulations

Temperature and Pressure Dependence of the Reaction between Ethyl Radical and Molecular Oxygen: Experiments and Master Equation Simulations

Automated Reaction Kinetics of Gas-Phase Organic Species over Multiwell Potential Energy Surfaces

Multifidelity neural network formulations for prediction of reactive molecular potential energy surfaces

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