Extensive High-Accuracy Thermochemistry and Group Additivity Values for Halocarbon Combustion Modeling

Accurate Enthalpies of Formation for PFAS from First-Principles: Combining Different Levels of Theory in a Generalized Thermochemical Hierarchy

The enthalpies of formation are computed for a large number of per- and poly fluoroalkyl substances (PFAS) using a connectivity-based hierarchy (CBH) approach. A combination of different electronic structure methods are used to provide the reference data in a hierarchical manner. The ANL0 method, in conjunction with the active thermochemical tables, provides enthalpies of formation for smaller species with subchemical accuracy. Coupled-cluster theory with explicit correlations are used to compute enthalpies of formation for intermediate species, based upon the ANL0 results. For the largest PFAS, including perfluorooctanoic acid (PFOA) and heptafluoropropylene oxide dimer acid (GenX), coupled-cluster theory with local correlations is used. The sequence of homodesmotic reactions proposed by the CBH are determined automatically by a new open-source code, AutoCBH. The results are the first reported enthalpies of formation for the majority of the species. A convergence analysis and global uncertainty quantification confirm that the enthalpies of formation at 0 K should be accurate to within ±5 kJ/mol. This new approach is not limited to PFAS, but can be applied to many chemical systems.

Implementing the Blowers-Masel Approximation to Scale Activation Energy Based on Reaction Enthalpy in Mean-Field Microkinetic Modeling for Catalytic Methane Partial Oxidation

Mean-field microkinetic modeling is a powerful tool for catalyst design and the simulation of catalytic processes. The reaction enthalpies in a microkinetic model often need to be adjusted when changing species' binding energies to model different catalysts, when performing thermodynamic sensitivity analyses, and when fitting experimental data. When altering reaction enthalpies, the activation energies should also be reasonably altered to ensure realistic reaction rates. The Blowers-Masel approximation (BMA) relates the reaction barrier to the reaction enthalpy. Unlike the Brønsted-Evans-Polani relationship, the BMA requires less data because only one parameter, the intrinsic activation energy, needs to be determined. We validate this application of BMA relations to model surface reactions by comparing against density functional theory data taken from the literature. By incorporating the BMA rate description into the open-source Cantera software, we enable a new workflow, demonstrated herein, allowing rapid screening of catalysts using linear scaling relationships and BMA kinetics within the process simulation software. For demonstration purposes, a catalyst screening for catalytic methane partial oxidation on 81 hypothetical metals is conducted. We compared the results with and without BMA-corrected rates. The heat maps of various descriptors (e.g., CH4 conversion, syngas yield) show that using BMA rates instead of Arrhenius rates (with constant activation energies) changes which metals are most active. Heat maps of sensitivity analyses can help identify which reactions or species are the most influential in shaping the descriptor map patterns. Our findings indicate that while using BMA-adjusted rates did not markedly affect the most sensitive reactions, it did change the most influential species.

Subgraph Isomorphic Decision Tree to Predict Radical Thermochemistry with Bounded Uncertainty Estimation

Detailed chemical kinetic models offer valuable mechanistic insights into industrial applications. Automatic generation of reliable kinetic models requires fast and accurate radical thermochemistry estimation. Kineticists often prefer hydrogen bond increment (HBI) corrections from a closed-shell molecule to the corresponding radical for their interpretability, physical meaning, and facilitation of error cancellation as a relative quantity. Tree estimators, used due to limited data, currently rely on expert knowledge and manual construction, posing challenges in maintenance and improvement. In this work, we extend the subgraph isomorphic decision tree (SIDT) algorithm originally developed for rate estimation to estimate HBI corrections. We introduce a physics-aware splitting criterion, explore a bounded weighted uncertainty estimation method, and evaluate aleatoric uncertainty-based and model variance reduction-based prepruning methods. Moreover, we compile a data set of thermochemical parameters for 2210 radicals involving C, O, N, and H based on quantum chemical calculations from recently published works. We leverage the collected data set to train the SIDT model. Compared to existing empirical tree estimators, the SIDT model (1) offers an automatic approach to generating and extending the tree estimator for thermochemistry, (2) has better accuracy and R2, (3) provides significantly more realistic uncertainty estimates, and (4) has a tree structure much more advantageous in descent speed. Overall, the SIDT estimator marks a great leap in kinetic modeling, offering more precise, reliable, and scalable predictions for radical thermochemistry.

Grammar of Impact Sensitivity: An Incremental Theory

In this paper, we report the first attempt to quantify impact sensitivity using the second-order incremental approach based on the structural features of explosives. It has been found that impact height (h50) can be expressed via a multiplicative incremental exponential form, in which the exponents are characteristic coefficients of structural increments multiplied by their numbers in the molecule. The method was developed on a large array of experimental data (450 molecules and salts) of different energetic materials, namely, nitro compounds, peroxides, nitrogen-rich salts, heterocycles, etc., while testing of the model was performed for 170 compounds. The results demonstrate a noticeable correlation with the experimental h50 values. Thus, the corresponding R2 and RMSE for the training and test sets are 0.56 (12.5 J) and 0.63 (18.8 J), respectively. In this work, we use 53 individual structural increments, but their number can be extended, and the corresponding coefficients can be refined; this allows for increasing the prediction accuracy on-the-fly. The calculation algorithm is discussed, and the corresponding examples are presented. The performed machine-based regression analysis using genetic function approximation, multiple linear regression, and artificial neural network has proven the reasonability and informativity of the proposed incremental theory. Thus, the developed approach significantly extends our understanding of the impact sensitivity phenomenon and translates it into the category of one that can be calculated by a pocket calculator.

Theory-Based Mechanism for Fluoromethane Combustion I: Thermochemistry and Abstraction Reactions

A new detailed chemical kinetic mechanism is presented for small fluorinated hydrocarbons. Ab initio electronic structure theory is used to provide heats of formation with subchemical accuracy. The ANL0 method is extended to include fluorine. The resulting heats of formation at 0 K are in excellent agreement with 36 benchmark species in the Active Thermochemical Tables, with a mean error of μ = -0.02 kJ/mol and a standard deviation of σ = 0.91 kJ/mol. The thermophysical properties for 92 small-molecule H/C/O/F species are computed. The rate coefficients for 40+ H-abstraction reactions involving H, O, F, OH, OF, HO₂, and various methyl radicals with CH₄, CH₃F, CH₂F₂, CHF₃, CH₂O, and CHFO are discussed. The computed rate constants are in excellent agreement with the available literature. Additionally, 30+ rate constants are provided for F abstraction, which are several orders of magnitude smaller than H abstraction. The thermophysical properties and rate constants are provided in a mechanism. This mechanism is the first in a series of theory-based investigations into the thermal destruction of per- and polyfluorinated species.

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.