Publications

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We present a new geodesic-based method for geometry optimization in a basis set of redundant internal coordinates. Our method updates the molecular geometry by following the geodesic generated by a displacement vector on the internal coordinate manifold, which dramatically reduces the number of steps required to converge to a minimum. Our method can be implemented in any existing optimization code, requiring only implementation of derivatives of the Wilson B-matrix and the ability to numerically solve an ordinary differential equation.

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We present a combined experimental and theoretical investigation of the autoignition chemistry of a prototypical cyclic hydrocarbon, cyclopentane. Experiments using a high-pressure photolysis reactor coupled to time-resolved synchrotron VUV photoionization mass spectrometry directly probe the short-lived radical intermediates and products in cyclopentane oxidation reactions. We detect key peroxy radical intermediates ROO and OOQOOH, as well as several hydroperoxides, formed by second O2 addition. Automated quantum chemical calculations map out the R + O2 + O2 reaction channels and demonstrate that the detected intermediates belong to the dominant radical chain-branching pathway: ROO (+ O2) → γ-QOOH + O2 → γ-OOQOOH → products. ROO, OOQOOH, and hydroperoxide products of second-O2 addition undergo extensive dissociative ionization, making their experimental assignment challenging. We use photoionization dynamics calculations to aid in their characterization and report the absolute photoionization spectra of isomerically pure ROO and γ-OOQOOH. A global statistical fit of the observed kinetics enables reliable quantification of the time-resolved concentrations of these elusive, yet critical species, paving the way for detailed comparisons with theoretical predictions from master-equation-based models.

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We developed a computational strategy to correlate bulk combustion metrics of novel fuels and blends in the low-temperature autoignition regime with measurements of key combustion intermediates in a small-volume, dilute, high-pressure reactor. We used neural net analysis of a large simulation dataset to obtain an approximate correlation and proposed experimental and computational steps needed to refine such a predictive correlation. We also designed and constructed a high-pressure laboratory apparatus to conduct the proposed measurements and demonstrated its performance on three canonical fuels: n-heptane, i-octane, and dimethyl ether.

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Chemical kinetics simulations are used to explore whether detailed measurements of relevant chemical species during the oxidation of very dilute fuels (less than 1 Torr partial pressure) in a high-pressure plug flow reactor (PFR) can predict autoignition propensity. We find that for many fuels the timescale for the onset of spontaneous oxidation in dilute fuel/air mixtures in a simple PFR is similar to the 1st-stage ignition delay time (IDT) at stoichiometric engine-relevant conditions. For those fuels that deviate from this simple trend, the deviation is closely related to the peak rate of production of OH, HO2, CH2O, and CO2 formed during oxidation. We use these insights to show that an accurate correlation between simulated profiles of these species in a PFR and 1st-stage IDT can be developed using convolutional neural networks. Our simulations suggest that the accuracy of such a correlation is 10–50%, which is appropriate for rapid fuel screening and may be sufficient for predictive fuel performance modeling.

KinBot is a Python code that automatically characterizes kinetically important stationary points on reactive potential energy surfaces and arranges the results into a form that lends itself easily to master equation calculations. This version of KinBot tackles C, H, O and S atom containing species and unimolecular (isomerization or dissociation) reactions. KinBot iteratively changes the geometry of the reactant to obtain initial guesses for reactive saddle points defined by KinBot's reaction types, which are then optimized by a third-party quantum chemistry package. KinBot verifies the connectivity of the saddle points with the reactant and identifies the products through intrinsic reaction coordinate calculations. New calculations can be automatically spawned from the products to obtain complete potential energy surfaces. The utilities of KinBot include conformer searches, projected frequency and hindered rotor calculations, and the automatic determination of the rotational symmetry numbers. Input files for popular RRKM master equation codes are automatically built, enabling an automated workflow all the way to the calculation of pressure and temperature dependent rate coefficients. Four examples are included. (i) [1,3]-sigmatropic H-migration reactions of unsaturated hydrocarbons and oxygenates are calculated to assess the relative importance of suprafacial and antrafacial reactions. (ii) Saddle points on three products of gamma-valerolactone thermal decomposition are studied and compared to literature potential energy surfaces. (iii) The previously published propene+OH reaction is reproduced to show the capability of building an entire potential energy surface. (iv) All species up to C4 in the Aramco Mech 2.0 are subjected to a KinBot search. Program summary: Program title: KinBot Program files doi: http://dx.doi.org/10.17632/hsh6dvv2zj.1 Licensing provisions: BSD 3-Clause Programming language: Python Supplementary material: 1. A static version of the source code (KinBot.tar), 2. The manual for the static version (KinBot_Manual.pdf) 3. Geometries and energies of the stationary points on the potential energy surface of the sigmatropic reaction search (sigmatropic_H_shift.out) 4. Geometries and energies of the stationary points on the potential energy surface of the propene+ OH central and terminal addition reaction (propene+oh central addition.out, propene+oh terminal addition.out) 5. Geometries and energies of the stationary points on the potential energy surface of gamma valerolactone, 4-pentenoic acid and 3-pentenoic acid (GVL energies and geometries.out, 4PA energies and geometries.out, 3PA energies and geometries.out) 6. Example runs including all input and output files for a one-well search for propanol radical, full PES search for the n-pentyl radical, a search for all homolytic scission in propanol, and the reaction searches for GVL (output.zip) 7. Results of symmetry calculations for a literature benchmark dataset (Symmetry_correct.pdf, Symmetry_wrong.pdf) Nature of problem: Automatic discovery of unimolecular reaction pathways (isomerization and dissociation) for molecules and radicals relevant in gas-phase combustion and atmospheric chemistry, including oxidation and pyrolytic processes for structures including carbon, oxygen, sulfur and hydrogen atoms. The reactants, products, and transition states are characterized using a suite of tools coupled to electronic structure codes, and the results are provided in a format that lends itself easily to calculating rate coefficients based on statistical rate theories with other external codes. Solution method: Reaction pathways are identified using heuristic searches starting from a reactant by iteratively altering its geometry toward a good guess for a transition state for reactions with barriers. The transition state is identified as a first-order saddle point on the potential energy surface, which is located using local optimization methods of third-party quantum chemistry codes. We use intrinsic reaction coordinate calculations to verify the direct connectivity of the saddle point to the reactant and to identify the product species. Conformational searches, hindered rotor potentials, frequency calculations, and high-level optimizations yield the necessary data for RRKM master equation calculations. Additional comments including restrictions and unusual features: KinBot is designed to run on Unix clusters, and is written in Python, compatible with versions 2.7 and 3. It communicates with a PBS or SLURM workload manager to submit quantum chemistry calculations to third-party software. It makes use of a modified fork of ASE for the input writing, calling and output parsing of the quantum chemistry software which has been tested with Gaussian (G09RevD.01). OpenBabel (2.4.1) and RDKit (2018.09.01) are used to convert smiles to internal species representations and for species comparison and results visualization. The output of KinBot can be visualized with the PESViewer script, and graph structures are drawn using NetworkX. The master equation solvers MESS or MESMER are needed to calculate rate coefficients at the end of a given run. This version of KinBot can handle H, C, S, and O atom-containing molecules, and searches for isomerization and dissociation pathways.

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Methanol is a benchmark for understanding tropospheric oxidation, but is underpredicted by up to 100% in atmospheric models. Recent work has suggested this discrepancy can be reconciled by the rapid reaction of hydroxyl and methylperoxy radicals with a methanol branching fraction of 30%. However, for fractions below 15%, methanol underprediction is exacerbated. Theoretical investigations of this reaction are challenging because of intersystem crossing between singlet and triplet surfaces – ∼45% of reaction products are obtained via intersystem crossing of a pre-product complex – which demands experimental determinations of product branching. Here we report direct measurements of methanol from this reaction. A branching fraction below 15% is established, consequently highlighting a large gap in the understanding of global methanol sources. These results support the recent high-level theoretical work and substantially reduce its uncertainties.

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Rate coefficients are key quantities in gas phase kinetics and can be determined theoretically via master equation (ME) calculations. Rate coefficients characterize how fast a certain chemical species reacts away due to collisions into a specific product. Some of these collisions are simply transferring energy between the colliding partners, in which case the initial chemical species can undergo a unimolecular reaction: dissociation or isomerization. Other collisions are reactive, and the colliding partners either exchange atoms, these are direct reactions, or form complexes that can themselves react further or get stabilized by deactivating collisions with a bath gas. The input of MEs are molecular parameters: geometries, energies, and frequencies determined from ab initio calculations. While the calculation of these rate coefficients using ab initio data is becoming routine in many cases, the determination of the uncertainties of the rate coefficients are often ignored, sometimes crudely assessed by varying independently just a few of the numerous parameters, and only occasionally studied in detail. In this study, molecular frequencies, barrier heights, well depths, and imaginary frequencies (needed to calculate quantum mechanical tunneling) were automatically perturbed in an uncorrelated fashion. Our Python tool, MEUQ, takes user requests to change all or specified well, barrier, or bimolecular product parameters for a reaction. We propagate the uncertainty in these input parameters and perform global sensitivity analysis in the rate coefficients for the ethyl + O₂ system using state-of-the-art uncertainty quantification (UQ) techniques via Python interface to UQ Toolkit (www.sandia.gov/uqtoolkit). A total of 10,000 sets of rate coefficients were collected after perturbing 240 molecular parameters. With our methodology, sensitive mechanistic steps can be revealed to a modeler in a straightforward manner for identification of significant and negligible influences in bimolecular reactions.

The high-level objective of this project is to solve national-security problems associated with petroleum use, cost, and environmental impacts by enabling more efficient use of natural-gas-fueled internal combustion engines. An improved science-base on end-gas autoignition, or “knock,” is required to support engineering of more efficient engine designs through predictive modeling. An existing optical diesel engine facility is retrofitted for natural gas fueling with laser-spark-ignition combustion to provide in-cylinder imaging and pressure data under knocking combustion. Zero-dimensional chemical-kinetic modeling of autoignition, adiabatically constrained by the measured cylinder pressure, isolates the role of autoignition chemistry. OH* chemiluminescence imaging reveals six different categories of knock onset that depend on proximity to engine surfaces and the in-cylinder deflagration. Modeling results show excellent prediction regardless of the knock category, thereby validating state-of-the-art kinetic mechanisms. The results also provide guidance for future work to build a science base on the factors that affect the deflagration rate.

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