RMG Database for Chemical Property Prediction.

The Reaction Mechanism Generator (RMG) database for chemical property prediction is presented. The RMG database consists of curated datasets and estimators for accurately predicting the parameters necessary for constructing a wide variety of chemical kinetic mechanisms. These datasets and estimators are mostly published and enable prediction of thermodynamics, kinetics, solvation effects, and transport properties. For thermochemistry prediction, the RMG database contains 45 libraries of thermochemical parameters with a combination of 4564 entries and a group additivity scheme with 9 types of corrections including radical, polycyclic, and surface absorption corrections with 1580 total curated groups and parameters for a graph convolutional neural network trained using transfer learning from a set of >130 000 DFT calculations to 10 000 high-quality values. Correction schemes for solvent-solute effects, important for thermochemistry in the liquid phase, are available. They include tabulated values for 195 pure solvents and 152 common solutes and a group additivity scheme for predicting the properties of arbitrary solutes. For kinetics estimation, the database contains 92 libraries of kinetic parameters containing a combined 21 000 reactions and contains rate rule schemes for 87 reaction classes trained on 8655 curated training reactions. Additional libraries and estimators are available for transport properties. All of this information is easily accessible through the graphical user interface at https://rmg.mit.edu. Bulk or on-the-fly use can be facilitated by interfacing directly with the RMG Python package which can be installed from Anaconda. The RMG database provides kineticists with easy access to estimates of the many parameters they need to model and analyze kinetic systems. This helps to speed up and facilitate kinetic analysis by enabling easy hypothesis testing on pathways, by providing parameters for model construction, and by providing checks on kinetic parameters from other sources.

Reaction Mechanism Generator v3.0: Advances in Automatic Mechanism Generation.

In chemical kinetics research, kinetic models containing hundreds of species and tens of thousands of elementary reactions are commonly used to understand and predict the behavior of reactive chemical systems. Reaction Mechanism Generator (RMG) is a software suite developed to automatically generate such models by incorporating and extrapolating from a database of known thermochemical and kinetic parameters. Here, we present the recent version 3 release of RMG and highlight improvements since the previously published description of RMG v1.0. Most notably, RMG can now generate heterogeneous catalysis models in addition to the previously available gas- and liquid-phase capabilities. For model analysis, new methods for local and global uncertainty analysis have been implemented to supplement first-order sensitivity analysis. The RMG database of thermochemical and kinetic parameters has been significantly expanded to cover more types of chemistry. The present release includes parallelization for faster model generation and a new molecule isomorphism approach to improve computational performance. RMG has also been updated to use Python 3, ensuring compatibility with the latest cheminformatics and machine learning packages. Overall, RMG v3.0 includes many changes which improve the accuracy of the generated chemical mechanisms and allow for exploration of a wider range of chemical systems.

Pressure-dependent kinetics of peroxy radicals formed in isobutanol combustion.

Bio-derived isobutanol has been approved as a gasoline additive in the US, but our understanding of its combustion chemistry still has significant uncertainties. Detailed quantum calculations could improve model accuracy leading to better estimation of isobutanol's combustion properties and its environmental impacts. This work examines 47 molecules and 38 reactions involved in the first oxygen addition to isobutanol's three alkyl radicals located α, ß, and γ to the hydroxide. Quantum calculations are mostly done at CCSD(T)-F12/cc-pVTZ-F12//B3LYP/CBSB7, with 1-D hindered rotor corrections obtained at B3LYP/6-31G(d). The resulting potential energy surfaces are the most comprehensive isobutanol peroxy networks published to date. Canonical transition state theory and a 1-D microcanonical master equation are used to derive high-pressure-limit and pressure-dependent rate coefficients, respectively. At all conditions studied, the recombination of γ-isobutanol radical with O2 forms HO2 + isobutanal. The recombination of ß-isobutanol radical with O2 forms a stabilized hydroperoxy alkyl radical below 400 K, water + an alkoxy radical at higher temperatures, and HO2 + an alkene above 1200 K. The recombination of ß-isobutanol radical with O2 results in a mixture of products between 700-1100 K, forming acetone + formaldehyde + OH at lower temperatures and forming HO2 + alkenes at higher temperatures. The barrier heights, high-pressure-limit rates, and pressure-dependent kinetics generally agree with the results from previous quantum chemistry calculations. Six reaction rates in this work deviate by over three orders of magnitude from kinetics in detailed models of isobutanol combustion, suggesting the rates calculated here can help improve modeling of isobutanol combustion and its environmental fate.

Phenyl radical + propene: a prototypical reaction surface for aromatic-catalyzed 1,2-hydrogen-migration and subsequent resonance-stabilized radical formation.

The C9H11 potential energy surface (PES) was experimentally and theoretically explored because it is a relatively simple, prototypical alkylaromatic radical system. Although the C9H11 PES has already been extensively studied both experimentally (under single-collision and thermal conditions) and theoretically, new insights were made in this work by taking a new experimental approach: flash photolysis combined with time-resolved molecular beam mass spectrometry (MBMS) and visible laser absorbance. The C9H11 PES was experimentally accessed by photolytic generation of the phenyl radical and subsequent reaction with excess propene (C6H5 + C3H6). The overall kinetics of C6H5 + C3H6 was measured using laser absorbance with high time-resolution from 300 to 700 K and was found to be in agreement with earlier measurements over a lower temperature range. Five major product channels of C6H5 + C3H6 were observed with MBMS at 600 and 700 K, four of which were expected: hydrogen (H)-abstraction (measured by the stable benzene, C6H6, product), methyl radical (CH3)-loss (styrene detected), H-loss (phenylpropene isomers detected) and radical adduct stabilization. The fifth, unexpected product observed was the benzyl radical, which was rationalized by the inclusion of a previously unreported pathway on the C9H11 PES: aromatic-catalysed 1,2-H-migration and subsequent resonance stabilized radical (RSR, benzyl radical in this case) formation. The current theoretical understanding of the C9H11 PES was supported (including the aromatic-catalyzed pathway) by quantitative comparisons between modelled and experimental MBMS results. At 700 K, the branching to styrene + CH3 was 2-4 times greater than that of any other product channel, while benzyl radical + C2H4 from the aromatic-catalyzed pathway accounted for ∼10% of the branching. Single-collision conditions were also simulated on the updated PES to explain why previous crossed molecular beam experiments did not see evidence of the aromatic-catalyzed pathway. This experimentally validated knowledge of the C9H11 PES was added to the database of the open-source Reaction Mechanism Generator (RMG), which was then used to generalize the findings on the C9H11 PES to a slightly more complicated alkylaromatic system.

Modeling study of the anti-knock tendency of substituted phenols as additives: an application of the reaction mechanism generator (RMG).

This work presents kinetic modeling efforts to evaluate the anti-knock tendency of several substituted phenols if used as gasoline additives. They are p-cresol, m-cresol, o-cresol, 2,4-xylenol, 2-ethylphenol, and guaiacol. A detailed kinetic model was constructed to predict the ignition of blends of the phenols in n-butane with the help of reaction mechanism generator (RMG), an open-source software package. The resulting model, which has 1465 species and 27 428 reactions, was validated against literature n-butane ignition data in the low-to-intermediate temperature range. To rank the anti-knock tendency of the additives, engine-like simulations were performed in a closed adiabatic homogenous batch reactor with a volume history derived from the pressure profile of a real research octane number (RON) engine test. The ignition timings of the additive blends were compared to that of primary reference fuels (PRFs) to quantitatively predict the anti-knock ability. The model predictions agree well with experimental determinations of the changes in RON induced by the additives. This study explains the chemical mechanism by which methyl-substituted phenols increase RON, and demonstrates how fundamental chemical kinetics can be used to evaluate practical fuel additive performance.